{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Clustering Stocks with Python\n",
    "\n",
    "Often we are given data sets that contain ungrouped or uncategorized data; in these cases, we may want to find some underlying structure that might not be apparent to the naked eyes. When we want to group objects into similar sets, we are said to be clustering. **The idea is relatively simple, objects that are more similar in nature should exist in the same group, and we use different measures of similarity to determine whether an object is similar to another.** While clustering as a concept is easy to grasp, there are different implementations to achieve this. Some applications use different algorithms or use various measures to define similarity. \n",
    "\n",
    "**In this tutorial, we will do a clustering exercise where we will take a group of stocks and cluster them into different groups based on specific financial metrics.** Clustering is used extensively in the financial industry to do a wide range of tasks, spanning from portfolio construction, outlier detection, or stock selection.\n",
    "\n",
    "Often, portfolio managers devise strategies to select stocks that build risk-adjusted portfolios; these are portfolios that minimize risk for a given level of return. However, to do this, they must choose stocks that are not correlated with each other or in some cases to find stocks that are similar in nature to give them adequate exposure to a particular segment of the market.\n",
    "\n",
    "Now, as mentioned above, there are many clustering algorithms we can use, but in this tutorial, we will use K-Means. **K-Means is an unsupervised machine learning algorithm which is used on unlabeled data (i.e., data without defined categories or groups). The goal of the K-Means algorithm is to divide `n` data points into `k` partitions, where the sum of the distances is minimized.**\n",
    "\n",
    "***\n",
    "\n",
    "**Broken into steps, the algorithm is executed in the following order:**\n",
    "\n",
    "1. Randomly select K-Centers, to be the cluster centers.\n",
    "2. Calculate the distance of each point to a cluster, and assign a cluster label where the Euclidean distance is smallest.\n",
    "3. Recompute the centroids by taking the mean of all the data points assigned to that cluster.\n",
    "4. Repeat these steps until one of the following two conditions are met:\n",
    "    1. *The Sum of the Distances is minimized.*\n",
    "    2. *The maximum number of iterations has been reached.*\n",
    "    \n",
    "    \n",
    "***\n",
    "\n",
    "The algorithm will converge to a result, but this result is not necessarily guaranteed to be the optimal result. K-Means is used heavily for exploratory data analysis and for its ability to take unstructured data and create structure from it. This is powerful when it comes to finding patterns in data that aren't necessarily apparent to the naked eye, especially at higher dimensions.\n",
    "\n",
    "## Libraries\n",
    "\n",
    "Luckily, performing cluster analysis in Python is easy due to built-in libraries that make running the algorithms efficient and fast. In this tutorial, we will use `pandas` to take data collected from our API and store it in a manner that makes manipulation manageable. To perform our cluster analysis, we will use `sklearn` which has built-in functions to create instances of our clustering algorithms. Along with this, we can perform a model evaluation with built-in metrics that will take our results and evaluate them.\n",
    "\n",
    "For some visualization tasks, we will use `matplotlib` and `yellowbrick` to create cluster graphs and silhouette graphs, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step One: Load the Stock Symbols\n",
    "I have a CSV file that contains one column of information which is stock symbols. Each stock symbol will be used for a request in the TD Ameritrade API to collect fundamental stock data and specific financial metrics. We will call pandas `read_csv` method to load the file, and store it in a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5808, 1)"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load the stock symbols into a data frame.\n",
    "stock_symbols = pd.read_csv(r\"C:\\Users\\Alex\\OneDrive\\Growth - Tutorial Videos\\Lessons - Python\\Python For Finance\\stock_list.csv\")\n",
    "\n",
    "# we have a character that will cause issues in our request so we have to remove it\n",
    "stock_symbols['Symbol'] = stock_symbols['Symbol'].str.replace('^','')\n",
    "\n",
    "# display the number of rows.\n",
    "display(stock_symbols.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With over 5800, stocks, we need to break this data frame into chunks so that we can request multiple symbols at once. While TD Ameritrade's **Search Instruments** endpoint does allow for numerous stock requests at once, we will have errors take place if we exceed 100, so let's break the data frame into chunks of 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the size of the chunk\n",
    "n = 100\n",
    "\n",
    "# for the sake of completeness\n",
    "\n",
    "# this defines a chunk, for example if i = 1 and n = 100 we would select rows 1 to 101 stock_symbosl[1:101]\n",
    "# stock_symbols[i:i+n]\n",
    "\n",
    "# this defines the whole range of the data frame. Start at 0, go to the last row(stock_symbols.shape[0]), \n",
    "# and take a step of n in this case 100\n",
    "# range( 0, stock_symbols.shape[0], n)\n",
    "\n",
    "# break the data frame into chunks\n",
    "symbols_chunk = [stock_symbols[i:i+n] for i in range( 0, stock_symbols.shape[0], n)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'AA', 'AABA', 'AAC', 'AAL']"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab an example so you can see the output\n",
    "example_chunk = list(symbols_chunk[0]['Symbol'])\n",
    "\n",
    "# show the first five items\n",
    "example_chunk[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Two: Collect the Data\n",
    "***\n",
    "Alright, we have a list of symbols so let's move to the next part collecting the data for each stock symbol. For those of us who have a TD Ameritrade account, we are in luck because they have a free API we can use to collect all sorts of data on different financial instruments. This particular API was very popular among viewers, so if you would like to learn more about all the endpoints and how to get set up, I encourage you to watch my series on YouTube which can be found here:\n",
    "\n",
    "**https://www.youtube.com/playlist?list=PLcFcktZ0wnNnrgVvY_87ZRXRlac6Pciur**\n",
    "\n",
    "The general idea, is we will loop through each chunk in our list, make a request using the symbols in that list, convert it to a dictionary object, grab the fundamental data that was sent back, store it in our master dictionary, and then finally take our master dictionary and convert it into a pandas data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define an endpoint, in this case we will use the instruments endpoint\n",
    "endpoint = r\"https://api.tdameritrade.com/v1/instruments\"\n",
    "\n",
    "# we need a place to store all of our data, so a dictionary will do.\n",
    "stock_dict = {}\n",
    "\n",
    "# loop through each chunk\n",
    "for chunk in symbols_chunk:\n",
    "    \n",
    "    # define the payload\n",
    "    payload = {'apikey':'API_Key',\n",
    "               'projection':'fundamental',\n",
    "               'symbol':list(chunk['Symbol'])}\n",
    "    \n",
    "    # make a request\n",
    "    content = requests.get(url = endpoint, params = payload)\n",
    "\n",
    "    # convert it dictionary object\n",
    "    data = content.json()\n",
    "\n",
    "    # the ones that do exist, loop through each stock, grab the data, and store it in the dictionary\n",
    "    try:\n",
    "        for stock in data:\n",
    "            stock_dict[data[stock]['cusip']] = data[stock]['fundamental']\n",
    "    except:\n",
    "        continue\n",
    "\n",
    "\n",
    "# create a data frame with the newly collected data.         \n",
    "stock_df = pd.DataFrame(stock_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the most part, the data is in a perfect format. However, we need to do one other transformation that involves transposing it. This will make selecting the columns of interest easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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       "      <th>...</th>\n",
       "      <th>revChangeYear</th>\n",
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       "      <th>shortIntToFloat</th>\n",
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       "      <th>totalDebtToCapital</th>\n",
       "      <th>totalDebtToEquity</th>\n",
       "      <th>vol10DayAvg</th>\n",
       "      <th>vol1DayAvg</th>\n",
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       "      <td>1.20455e+06</td>\n",
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       "      <td>50.0643</td>\n",
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       "              beta bookValuePerShare currentRatio divGrowthRate3Year  \\\n",
       "037833100  1.22691           2.79895      1.31538                  0   \n",
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       "464288182  0.94922                 0            0                  0   \n",
       "\n",
       "          dividendAmount           dividendDate dividendPayAmount  \\\n",
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       "464288182        1.33543  2018-12-18 00:00:00.0          0.927511   \n",
       "\n",
       "                 dividendPayDate dividendYield epsChange  ... revChangeYear  \\\n",
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       "\n",
       "          sharesOutstanding shortIntDayToCover shortIntToFloat symbol  \\\n",
       "037833100           4601.07                  0               0   AAPL   \n",
       "00773U108           63.7808                  0               0   ADVM   \n",
       "05464C101           59.1274                  0               0   AAXN   \n",
       "003881307           50.0643                  0               0   ACTG   \n",
       "464288182              58.8                  0               0   AAXJ   \n",
       "\n",
       "          totalDebtToCapital totalDebtToEquity  vol10DayAvg   vol1DayAvg  \\\n",
       "037833100            51.5493           106.395  2.43502e+07  2.43016e+07   \n",
       "00773U108                  0                 0       693916       693690   \n",
       "05464C101                  0                 0  1.20455e+06  1.20414e+06   \n",
       "003881307                  0                 0        97551        97430   \n",
       "464288182                  0                 0       906716       905520   \n",
       "\n",
       "          vol3MonthAvg  \n",
       "037833100  6.03592e+08  \n",
       "00773U108  1.34349e+07  \n",
       "05464C101  1.46130e+07  \n",
       "003881307  2.81597e+06  \n",
       "464288182  2.96877e+07  \n",
       "\n",
       "[5 rows x 46 columns]"
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   "source": [
    "# it's not in the right format, but if we transpose it we will have a much easier time grabbing the necessary columns.\n",
    "stock_df = stock_df.transpose()\n",
    "\n",
    "# display the head to make sure it's right\n",
    "display(stock_df.head())\n",
    "\n",
    "# let's recheck the shape, we lost a few hundred but this was expected as TD Ameritrade doesn't have data on every stock.\n",
    "display(stock_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Three: Select the Attributes\n",
    "***\n",
    "We now have a master data frame that contains all the data on our stock. There are over 46  attributes we can choose from, or if we wanted, we could select them all. However, we will only be selecting three attributes in this tutorial. I want to make sure we understand certain concepts before we move on to higher dimensional data, and it’ll be easier to visualize these concepts with fewer attributes.\n",
    "\n",
    "**With that being said, I did have the luxury of testing multiple combinations of attributes, and one set that gave promising results was Return on Equity, Return on Assets, and Return on Investment.** \n",
    "\n",
    "If you’re curious what these metrics are, I provided some definitions found on **Investopedia**.\n",
    "\n",
    "1. **Return on Assets:** Return on assets (ROA) is an indicator of how profitable a company is relative to its total assets. ROA gives a manager, investor, or analyst an idea as to how efficient a company’s management is at using its assets to generate earnings. Return on assets is displayed as a percentage.<br></br>\n",
    "\n",
    "\n",
    "2. **Return on Equity:** Return on equity (ROE) is a measure of financial performance calculated by dividing net income by shareholders’ equity. Because shareholders’ equity is equal to a company’s assets minus its debt, ROE could be thought of as the return on net assets. ROE is considered a measure of how effectively management is using a company’s assets to create profits.\n",
    "\n",
    "\n",
    "3. **Return on Investments:** Return on Investment (ROI) is a performance measure used to evaluate the efficiency of an investment or compare the efficiency of a number of different investments. ROI tries to directly measure the amount of return on a particular investment, relative to the investment’s cost. To calculate ROI, the benefit (or return) of an investment is divided by the cost of the investment. The result is expressed as a percentage or a ratio.\n",
    "\n",
    "When I wanted to select attributes, I wanted to select attributes that gave a different perspective of the company, and could be standardized across different types of companies.\n",
    "\n",
    "Also, make sure we choose the `symbol` column so we can use it as an index in our data frame. K-Means cannot handle missing data so it must be either removed from the data set or extra steps must be taken to fill in the missing values with either average values or estimates. To remove our missing data, we need to filter the data frame to remove any zeros, and then drop any `na` values by using the `dropna` method.\n",
    "\n",
    "This was honestly a very interesting part of the tutorial, so much so that I explored some research papers that helped influence my selection of attributes. If you get a chance, I highly encourage you to read some of these papers and see their findings.\n",
    "\n",
    "- https://www.researchgate.net/publication/4885243_Stock_selection_based_on_cluster_analysis\n",
    "- https://www.researchgate.net/publication/316705565_The_Classification_of_Stocks_with_Basic_Financial_Indicators_An_Application_of_Cluster_Analysis_on_the_BIST_100_Index"
   ]
  },
  {
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       "      <td>0</td>\n",
       "      <td>ACTG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464288182</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AAXJ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          returnOnEquity returnOnAssets returnOnInvestment symbol\n",
       "037833100        48.1829        15.8055            21.3029   AAPL\n",
       "00773U108              0              0                  0   ADVM\n",
       "05464C101        6.76543         4.0626            5.47783   AAXN\n",
       "003881307              0              0                  0   ACTG\n",
       "464288182              0              0                  0   AAXJ"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returnOnEquity</th>\n",
       "      <th>returnOnAssets</th>\n",
       "      <th>returnOnInvestment</th>\n",
       "      <th>symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>037833100</th>\n",
       "      <td>48.1829</td>\n",
       "      <td>15.8055</td>\n",
       "      <td>21.3029</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05464C101</th>\n",
       "      <td>6.76543</td>\n",
       "      <td>4.0626</td>\n",
       "      <td>5.47783</td>\n",
       "      <td>AAXN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00770C101</th>\n",
       "      <td>45.4348</td>\n",
       "      <td>30.1678</td>\n",
       "      <td>36.0431</td>\n",
       "      <td>ADES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00081T108</th>\n",
       "      <td>12.9167</td>\n",
       "      <td>3.53989</td>\n",
       "      <td>4.31677</td>\n",
       "      <td>ACCO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>049164205</th>\n",
       "      <td>12.0875</td>\n",
       "      <td>4.19331</td>\n",
       "      <td>4.94682</td>\n",
       "      <td>AAWW</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          returnOnEquity returnOnAssets returnOnInvestment symbol\n",
       "037833100        48.1829        15.8055            21.3029   AAPL\n",
       "05464C101        6.76543         4.0626            5.47783   AAXN\n",
       "00770C101        45.4348        30.1678            36.0431   ADES\n",
       "00081T108        12.9167        3.53989            4.31677   ACCO\n",
       "049164205        12.0875        4.19331            4.94682   AAWW"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define our indicators list\n",
    "metrics_list = ['returnOnEquity','returnOnAssets','returnOnInvestment','symbol']\n",
    "\n",
    "# select only those columns\n",
    "indicators_df = stock_df[metrics_list]\n",
    "\n",
    "# display the unedited DF\n",
    "display(indicators_df.head())\n",
    "\n",
    "# clustering can't handle missing values, so we need to eliminate any row that has a missing value.\n",
    "indicators_df = indicators_df[indicators_df[metrics_list] != 0]\n",
    "indicators_df = indicators_df.dropna(how='any')\n",
    "\n",
    "display(indicators_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, to make things more manageable, let's set the `symbol` column as the data frame index. This will make selecting certain stock symbols much easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returnOnEquity</th>\n",
       "      <th>returnOnAssets</th>\n",
       "      <th>returnOnInvestment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>48.18294</td>\n",
       "      <td>15.80550</td>\n",
       "      <td>21.30287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAXN</th>\n",
       "      <td>6.76543</td>\n",
       "      <td>4.06260</td>\n",
       "      <td>5.47783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADES</th>\n",
       "      <td>45.43475</td>\n",
       "      <td>30.16776</td>\n",
       "      <td>36.04312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACCO</th>\n",
       "      <td>12.91672</td>\n",
       "      <td>3.53989</td>\n",
       "      <td>4.31677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAWW</th>\n",
       "      <td>12.08748</td>\n",
       "      <td>4.19331</td>\n",
       "      <td>4.94682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        returnOnEquity  returnOnAssets  returnOnInvestment\n",
       "symbol                                                    \n",
       "AAPL          48.18294        15.80550            21.30287\n",
       "AAXN           6.76543         4.06260             5.47783\n",
       "ADES          45.43475        30.16776            36.04312\n",
       "ACCO          12.91672         3.53989             4.31677\n",
       "AAWW          12.08748         4.19331             4.94682"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the index\n",
    "indicators_df = indicators_df.set_index('symbol')\n",
    "\n",
    "# do a data type conversion\n",
    "indicators_df = indicators_df.astype('float')\n",
    "\n",
    "indicators_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Four: Remove Outliers\n",
    "***\n",
    "Now comes the exciting part, removing extreme values. The reality is that our data has many extreme values and this is expected given the variety of our companies. For example, our Return on Equity indicator has one company that has a value of 16000%! This value is not reflective of a traditional company, and more than likely is due to the fact of having a small equity base with large revenue denominator.\n",
    "\n",
    "We need to remove these extreme values from our data set or else they will skew our results. However, that begs the question of what is a \"reasonable value\"? If we do a little exploring on the internet, we find that NYU Stern has provided an industry benchmark for a wide range of financial metrics. This will serve as our baseline for removing extreme values from the data sets.\n",
    "\n",
    "If you're interested in seeing this benchmarks, please go to the following link:\n",
    ">*http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/pbvdata.html*\n",
    ">*http://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile/pedata.html*\n",
    "\n",
    "\n",
    "Let's get an idea of just how many \"extreme\" values exist in our data set by filtering the different columns and performing a `value_counts`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     1906\n",
       "False     193\n",
       "Name: returnOnEquity, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True     2055\n",
       "False      44\n",
       "Name: returnOnAssets, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True     2018\n",
       "False      81\n",
       "Name: returnOnInvestment, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the filters\n",
    "roe_filter = indicators_df.iloc[:,0] < 40\n",
    "roa_filter = indicators_df.iloc[:,1] < 30\n",
    "roi_filter = indicators_df.iloc[:,2] < 30\n",
    "\n",
    "# get the counts for each column\n",
    "roe_count = pd.Series(roe_filter).value_counts()\n",
    "roa_count = pd.Series(roa_filter).value_counts()\n",
    "roi_count = pd.Series(roi_filter).value_counts()\n",
    "\n",
    "# display the results\n",
    "display(roe_count)\n",
    "display(roa_count)\n",
    "display(roi_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Alex\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returnOnEquity</th>\n",
       "      <th>returnOnAssets</th>\n",
       "      <th>returnOnInvestment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAXN</th>\n",
       "      <td>6.76543</td>\n",
       "      <td>4.06260</td>\n",
       "      <td>5.47783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACCO</th>\n",
       "      <td>12.91672</td>\n",
       "      <td>3.53989</td>\n",
       "      <td>4.31677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAWW</th>\n",
       "      <td>12.08748</td>\n",
       "      <td>4.19331</td>\n",
       "      <td>4.94682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADUS</th>\n",
       "      <td>7.51694</td>\n",
       "      <td>5.33343</td>\n",
       "      <td>6.52183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB</th>\n",
       "      <td>15.52055</td>\n",
       "      <td>15.50523</td>\n",
       "      <td>15.50523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        returnOnEquity  returnOnAssets  returnOnInvestment\n",
       "symbol                                                    \n",
       "AAXN           6.76543         4.06260             5.47783\n",
       "ACCO          12.91672         3.53989             4.31677\n",
       "AAWW          12.08748         4.19331             4.94682\n",
       "ADUS           7.51694         5.33343             6.52183\n",
       "AB            15.52055        15.50523            15.50523"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the entire data frame\n",
    "indicators_df = indicators_df[roe_filter & roa_filter & roi_filter]\n",
    "\n",
    "# display the results\n",
    "indicators_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, after removing some of these extreme values, we are now at an excellent spot to do a statistical summary of our data set. Use the `describe` method to create a summary data frame and then add a standard deviation metric that is calculated using the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returnOnEquity</th>\n",
       "      <th>returnOnAssets</th>\n",
       "      <th>returnOnInvestment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "      <td>1883.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.593978</td>\n",
       "      <td>5.968243</td>\n",
       "      <td>7.581129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.704187</td>\n",
       "      <td>4.584551</td>\n",
       "      <td>5.827219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.044180</td>\n",
       "      <td>0.036950</td>\n",
       "      <td>0.041490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.753440</td>\n",
       "      <td>2.578305</td>\n",
       "      <td>3.137180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11.049520</td>\n",
       "      <td>4.767950</td>\n",
       "      <td>6.149230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.591245</td>\n",
       "      <td>8.169765</td>\n",
       "      <td>10.518510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.538890</td>\n",
       "      <td>26.093900</td>\n",
       "      <td>29.889750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>+3_std</th>\n",
       "      <td>38.706541</td>\n",
       "      <td>19.721895</td>\n",
       "      <td>25.062786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-3_std</th>\n",
       "      <td>-13.518584</td>\n",
       "      <td>-7.785410</td>\n",
       "      <td>-9.900528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        returnOnEquity  returnOnAssets  returnOnInvestment\n",
       "count      1883.000000     1883.000000         1883.000000\n",
       "mean         12.593978        5.968243            7.581129\n",
       "std           8.704187        4.584551            5.827219\n",
       "min           0.044180        0.036950            0.041490\n",
       "25%           5.753440        2.578305            3.137180\n",
       "50%          11.049520        4.767950            6.149230\n",
       "75%          17.591245        8.169765           10.518510\n",
       "max          39.538890       26.093900           29.889750\n",
       "+3_std       38.706541       19.721895           25.062786\n",
       "-3_std      -13.518584       -7.785410           -9.900528"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a statistical summary\n",
    "desc_df = indicators_df.describe()\n",
    "\n",
    "# add the standard deviation metric\n",
    "desc_df.loc['+3_std'] = desc_df.loc['mean'] + (desc_df.loc['std'] * 3)\n",
    "desc_df.loc['-3_std'] = desc_df.loc['mean'] - (desc_df.loc['std'] * 3)\n",
    "\n",
    "# display it\n",
    "desc_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Five: Visualize & Scale the Data\n",
    "***\n",
    "For the most part, the data is in a good range, there are a few values that are outside three standard deviations, but it's expected given the variety of stocks we have. Also, we will take steps later to mitigate this. Now that we've done a statistical summary, we can move on to plotting. In this example, let's use a 3D scatter plot to see all the data points. I will be using `matplotlib` to create our chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "# define a figure and a 3D axis\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "# define the x, y, & z of our scatter plot, this will just be the data from our data frame.\n",
    "x = list(indicators_df.iloc[:,0])\n",
    "y = list(indicators_df.iloc[:,1])\n",
    "z = list(indicators_df.iloc[:,2])\n",
    "\n",
    "# define the axis labels\n",
    "column_names = indicators_df.columns\n",
    "ax.set_xlabel(column_names[0])\n",
    "ax.set_ylabel(column_names[1])\n",
    "ax.set_zlabel(column_names[2])\n",
    "\n",
    "# define the markers, and the color\n",
    "ax.scatter(x, y, z, c='royalBlue', marker='o')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, I purposely limited the number of attributes in our data set. The reason being that it's hard to visualize data that exceeds three dimensions. However, looking at this data it does seem to be clumped together with no definite spherical shape, this can cause issues when clustering because naturally, the algorithm looks for the precise structure to cluster the data around. There are steps we can take to help mitigate this issue, but this also a reality of data, there might not be clear clusters, and in some cases, clustering might not be the right approach.\n",
    "\n",
    "Let's continue and begin normalizing our data, to help handle outliers and things of that nature. You should almost always normalize your data as the model tends to perform better with normalized data vice the alternative. We have a few options to normalize our data:\n",
    "\n",
    "1. **Standard Scaler:** Here we subtract the mean from each data point and divide it by the standard deviation. This method is sensitive to outliers; however, like computing, the mean means taking the average of ALL data points, including the outliers.\n",
    "\n",
    "2. **Min Max Scaler:** Here we scale the data so that it fits in a range between 0 and 1. Mathematically we take each data point, subtract the minimum from it, and then divide it by the difference of the maximum value and the minimum value. Again, this is sensitive to outliers as the maximum amount would be the outlier.\n",
    "\n",
    "3. **Robust Scaler:** This method is a better choice if your data has outliers. With this method, we use the interquartile range instead of the minimum and maximum, which helps control for outliers.\n",
    "\n",
    "In our example, because there might be a possibility for outliers, I recommend we use the Robust Scaling method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
    "\n",
    "# for demonstration purposes, I will be creating all three instances of the scalers.\n",
    "min_max_scaler = MinMaxScaler()\n",
    "std_scaler = StandardScaler()\n",
    "robust_scaler = RobustScaler()\n",
    "\n",
    "# scale the data\n",
    "X_train_minmax = min_max_scaler.fit_transform(indicators_df)\n",
    "X_train_standard = std_scaler.fit_transform(indicators_df)\n",
    "X_train_robust = robust_scaler.fit_transform(indicators_df)\n",
    "\n",
    "# create a new plot\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "# take the scaled data in this example.\n",
    "x = X_train_robust[:,0]\n",
    "y = X_train_robust[:,1]\n",
    "z = X_train_robust[:,2]\n",
    "\n",
    "# define the axes labels\n",
    "column_names = indicators_df.columns\n",
    "ax.set_xlabel(column_names[0])\n",
    "ax.set_ylabel(column_names[1])\n",
    "ax.set_zlabel(column_names[2])\n",
    "\n",
    "# create a new plot\n",
    "ax.scatter(x, y, z, c='royalBlue')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Six (Optional): Principal Component Analysis\n",
    "***\n",
    "By scaling the data, we are allowing our algorithm to perform better with the data. Another topic we will discuss is the idea of Principal Component Analysis, which, with large data sets with multiple attributes, will allow our model to run faster and have less redundant data.\n",
    "\n",
    "To help conceptualize this concept, imagine I described a particular famous dog to you.\n",
    "\n",
    " > **Clifford is a big red dog with a friendly smile.**\n",
    " \n",
    "Now imagine, I added more detail to the story to help describe Clifford.\n",
    "\n",
    " > **Clifford is a big fire-red dog with a friendly smile.**\n",
    " \n",
    "For the most part, many people would say that adding the extra detail didn't provide much more to the story. I bet I could've left it out entirely and you still would've got the gist of the story. With PCA, we try to determine this additional info and remove it from the story. Yes, we might remove attributes from our data set, but it's to our benefit in most cases.\n",
    "\n",
    "The idea is that even though I might lose 3% of all information I still can describe 97% of the story and in most cases, this is more than enough to get the general theme of the story. We also gain the benefit of faster execution time when it comes to training our model. In a clustering algorithm like K-Means, while it is easy to understand and implement, it does suffer from long training runs when there are too many attributes.\n",
    "\n",
    "PCA attempts to remedy this problem by removing the attributes that don't contribute too much to story and keep those attributes which contribute significantly to the story. Implementing PCA in `sklearn` is easy to do, but something we need to do as an initial step is to get an understanding of the number of components we need.\n",
    "\n",
    "Visually this can be done by passing through our scaled data set into our PCA class object, and plotting the `explained_variance_ratio` which will tell us how much of the variance is explained at any given number of components.\n",
    "\n",
    "Let's implement this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# pass through the scaled data set into our PCA class object\n",
    "pca = PCA().fit(X_train_robust)\n",
    "\n",
    "# plot the Cumulative Summation of the Explained Variance\n",
    "plt.figure()\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "\n",
    "# define the labels & title\n",
    "plt.xlabel('Number of Components', fontsize = 15)\n",
    "plt.ylabel('Variance (%)', fontsize = 15) \n",
    "plt.title('Explained Variance', fontsize = 20)\n",
    "\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the chart above, we can see that we have 100% of variance explained with only two components. This means that if we were to implement a PCA, we would select our number of components to be 2. In most examples, we won't get 100% explained the variance, but the general rule is to choose the minimum number of components that demonstrates the highest amount of variance.\n",
    "\n",
    "**I will only be using PCA for demonstration purposes, in a case with such few attributes as our example PCA is not justified or recommended.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a PCA modified dataset\n",
    "pca_dataset = PCA(n_components=2).fit(X_train_robust).transform(X_train_robust)\n",
    "\n",
    "# store it in a new data frame\n",
    "pca_dataset= pd.DataFrame(data = pca_dataset, columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By running PCA, we have reduced the number of dimensions in our data set from 3 to 2; this means if we graph our new data frame, we will only have two dimensions. Let's see how the new data set would look graphically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e2386c6588>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define a figure\n",
    "plt.figure()\n",
    "\n",
    "# define the label and title\n",
    "plt.xlabel('Principal Component 1', fontsize = 15)\n",
    "plt.ylabel('Principal Component 2', fontsize = 15)\n",
    "plt.title('2 Component PCA', fontsize = 20)\n",
    "\n",
    "# plot the figure\n",
    "plt.scatter(pca_dataset['principal component 1'], pca_dataset['principal component 2'], c='royalBlue', s = 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Seven: Build & Run the Model\n",
    "***\n",
    "One of the biggest challenges with K-Means is determining the optimum number of clusters, so I decided to explore some options we have. One type of analysis we can do is related to silhouette analysis, and while I would like to describe it myself, I think the official `sklearn` documentation does a beautiful job of summarizing it.\n",
    "\n",
    "Silhouette analysis can be used to study the separation distance between the resulting clusters. The silhouette plot displays a measure of how close each point in one cluster is to points in the neighboring clusters and thus provides a way to assess parameters like the number of clusters visually. This measure has a range of (-1, 1).\n",
    "\n",
    "Silhouette coefficients (as these values are referred to as) near +1 indicate that the sample is far away from the neighboring clusters. A value of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters and negative values indicate that those samples might have been assigned to the wrong cluster.\n",
    "\n",
    "If you would like a more detailed explanation, feel free to explore the documentation yourself.\n",
    "<br>*https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html*</br>\n",
    "\n",
    "We will create an instance of our KMeans model, and explore how the silhouette score changes within a limited range of clusters. From here, we will take the maximum score to determine our cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 2\n",
      "Silhouette Score: 0.5410521499110389\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 3\n",
      "Silhouette Score: 0.4716258464034461\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 4\n",
      "Silhouette Score: 0.40543399274059067\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 5\n",
      "Silhouette Score: 0.4172364603093746\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 6\n",
      "Silhouette Score: 0.3950713176986843\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 7\n",
      "Silhouette Score: 0.36067858847327827\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 8\n",
      "Silhouette Score: 0.37326403526244045\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 9\n",
      "Silhouette Score: 0.3641543297087075\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "\n",
    "# define a dictionary that contains all of our relevant info.\n",
    "results_dict = {}\n",
    "\n",
    "# define how many clusters we want to test up to.\n",
    "num_of_clusters = 10\n",
    "\n",
    "# run through each instance of K\n",
    "for k in range(2, num_of_clusters):\n",
    "    \n",
    "    print(\"-\"*100)\n",
    "    \n",
    "    # define the next dictionary to hold all the results of this run.\n",
    "    results_dict[k] = {}\n",
    "\n",
    "    # create an instance of the model, and fit the training data to it.\n",
    "    kmeans = KMeans(n_clusters=k, random_state=0).fit(X_train_robust)\n",
    "    \n",
    "    # define the silhouette score\n",
    "    sil_score = metrics.silhouette_score(X_train_robust, kmeans.labels_, metric='euclidean')\n",
    "    \n",
    "    # store the different metrics\n",
    "    results_dict[k]['silhouette_score'] = sil_score\n",
    "    results_dict[k]['inertia'] = kmeans.inertia_\n",
    "    results_dict[k]['score'] = kmeans.score\n",
    "    results_dict[k]['model'] = kmeans\n",
    "    \n",
    "    # print the results    \n",
    "    print(\"Number of Clusters: {}\".format(k))\n",
    "    print('Silhouette Score:', sil_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so we ran our cluster analysis now what? Well, the first thing we should look at is the overall silhouette score, **ideally the larger the number, the better the results. However, this score is just part of the story, and we will have to examine the results to give ourselves a sanity check visually, but at this point, we can use the silhouette score as a gage of which ones we should target first.**\n",
    "\n",
    "Looking at the results above, 2 or 3 should be our target.\n",
    "\n",
    "Let's also run the results on our PCA data set to see what the output would look like. In this case, we get a higher silhouette score, which is a good sign, and it points to the same outcome, explore a cluster of 2 or 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 2\n",
      "Silhouette Score: 0.549296276500416\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 3\n",
      "Silhouette Score: 0.48451707190440624\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 4\n",
      "Silhouette Score: 0.4213752106918571\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 5\n",
      "Silhouette Score: 0.4344994798387365\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 6\n",
      "Silhouette Score: 0.4162688230552962\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 7\n",
      "Silhouette Score: 0.3924659597501012\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 8\n",
      "Silhouette Score: 0.3977385129679456\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Number of Clusters: 9\n",
      "Silhouette Score: 0.39078524881622423\n"
     ]
    }
   ],
   "source": [
    "# define a dictionary that contains all of our relevant info.\n",
    "results_dict_pca = {}\n",
    "\n",
    "# define how many clusters we want to test up to.\n",
    "num_of_clusters = 10\n",
    "\n",
    "# run through each instance of K\n",
    "for k in range(2, num_of_clusters):\n",
    "    \n",
    "    print(\"-\"*100)\n",
    "    \n",
    "    # define the next dictionary to hold all the results of this run.\n",
    "    results_dict_pca[k] = {}\n",
    "\n",
    "    # create an instance of the model, and fit the training data to it.\n",
    "    kmeans = KMeans(n_clusters=k, random_state=0).fit(pca_dataset)\n",
    "    \n",
    "    # define the silhouette score\n",
    "    sil_score = metrics.silhouette_score(pca_dataset, kmeans.labels_, metric='euclidean')\n",
    "    \n",
    "    # store the different metrics\n",
    "    results_dict_pca[k]['silhouette_score'] = sil_score\n",
    "    results_dict_pca[k]['inertia'] = kmeans.inertia_\n",
    "    results_dict_pca[k]['score'] = kmeans.score\n",
    "    results_dict_pca[k]['model'] = kmeans\n",
    "    \n",
    "    # print the results    \n",
    "    print(\"Number of Clusters: {}\".format(k))\n",
    "    print('Silhouette Score:', sil_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Eight: Model Evaluation\n",
    "With the help of a graphical aid, we can also analyze the results of our clusters. If we use the `yellowbrick` library, we get to access the `SilhouetterVisualizer` which will help visualize the silhouette score for each point in that particular cluster. What we are looking for is that each cluster exceeds the red line or the average silhouette score and that the clusters are as evenly distributed as possible.\n",
    "\n",
    "You may be wondering why some of these values are below 0, and some are above, with the negative values these are data points that visually fall at the edge of two clusters. In other words, it's hard to determine where they fall; they are outliers that are tricky to group.\n",
    "\n",
    "The high positive values are data points that would almost be in the center of the cluster and very easy to classify. I've provided a visual aid to help drive home the concept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.cluster import SilhouetteVisualizer\n",
    "\n",
    "clusters = [2,3]\n",
    "\n",
    "for cluster in clusters:\n",
    "    \n",
    "    print('-'*100)\n",
    "\n",
    "    # define the model for K\n",
    "    kmeans = KMeans(n_clusters = cluster, random_state=0)\n",
    "\n",
    "    # pass the model through the visualizer\n",
    "    visualizer = SilhouetteVisualizer(kmeans)\n",
    "\n",
    "    # fit the data\n",
    "    visualizer.fit(X_train_robust)\n",
    "\n",
    "    # show the chart\n",
    "    visualizer.poof()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = [2,3]\n",
    "\n",
    "for cluster in clusters:\n",
    "\n",
    "    print('-'*100)\n",
    "\n",
    "    # define the model for K\n",
    "    kmeans = KMeans(n_clusters = cluster, random_state=0)\n",
    "\n",
    "    # pass the model through the visualizer\n",
    "    visualizer = SilhouetteVisualizer(kmeans)\n",
    "\n",
    "    # fit the data\n",
    "    visualizer.fit(pca_dataset)\n",
    "\n",
    "    # show the chart\n",
    "    visualizer.poof()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Nine: Chart the Clusters\n",
    "***\n",
    "Let's get to the fun part, visualizing our clusters! This will involve, in the non-PCA case, creating another 3D scatter plot, but in this instance, we will define the `c` parameter of our scatter to equal our model labels. This will correctly create the clusters for us.\n",
    "\n",
    "Again, we will do this in the case of 2 and 3 clusters so we can visually see the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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1tllfX8+5557L2rVrWbRokeFWMX/J4/HWW28BsGTJEi655BIuueQSXn31Va6++mpKS0v5xS9+wbp16xg5cqSxXnjGGWeQn5/P/fff38P9+sMf/pCrrrqKZcuWsWDBAgRB4P/+7/+oq6tL616dfvrp3Hbbbaxbt46f/exnuN1uHn300R4TYDxuuukm5s6dy2OPPcZvfvMb5syZw0033dRjuwsvvJApU6bw8MMP88ILL0RZaQsWLOD444/nkUce4frrr6e2tpb7778fgFNOOYXKykp++9vf0tjYyJNPPsnw4cO59dZb+fjjj7n55pv59re/Hffc7rjjDk499VQee+wxfv/733Puuefy/e9/H7vdziOPPEJNTQ0LFy5k586dHH744XGPMXr0aO69916ampq4/fbbOfjggw3xSfYcDBo0iOOOO441a9awePFiLrvsMkaMGME999zD0qVLOeSQQ9i8eXPUZK5z0kknARiu5aOOOirq97HMnj2bIUOG8NRTT/W6zmymvLycxx57DFVVue6662hvb+fhhx9m9OjRPbZ94IEHOOKII7jnnnt45plnOP/887n88st7bPfDH/6Qb33rW9x22238+9//ZtasWcZnd955J1OmTOHuu+/m1ltvZcqUKdx2221A9HMuSRJPPfUUeXl53HDDDWzfvp377ruP2traHuMVFxfzwAMPYLPZuP7663nmmWc455xzuPjii1O+DxbZQ+jFVWU1orWwsLCw2BdJuAZiBbdY7NdomoaqqoTDYSRJQpKkHmuOFhYWBxaWxWexX6ILXiQSQdM0I5dL0zREUTT+EwQBURQtQbSw2P+wLD6LAwNN01AUha1bt1JbW2tECgLGWip0B3boQTiapuH3+/H5fNTW1kYJos1mi9rPwsJi38cSPov9Al3wFEVB07QeJbiSCZcgCGiaRiQS6SGI+udmC1F3mVqCaGGxb2IJn8U+Tazg6WKki1m6xzJjFjWzIOpu00SCGK+iiIWFxcDBEj6LfZJEgqeTrvClY7llIoi6y9TCwmLvYwmfxT5Fb4LX12P3hVQFMRAI4HQ6ycvLswTRwmIvYAmfxT6BLiR6+a7eBC+XFl+6xApic3MzJSUlCdcQbTabsYZoYWGRfSzhsxjQqKqKoiioqmpYeKlgFr5U9+mrxZcu6bhM9ZQLSxAtLPqOJXwWA5J4gpfuOtxAsfjSJZEgxrMQLUG0sEgfS/gsBhR9Fby+0N8WX7rEE8TYNlS6AFqCaGGRGEv4LAYE2Ra8gW7xZUtkY89b76SgH7+xsZHy8nIKCgosQbSw+BpL+Cz2KmbB08nGhJxJHt/+hLmvoB4JC9HuUl0ILUG0ONCwhM9ir6CqKrt376aioiJnCd+xbsBkQtifQtmfwhIbEBTbENgSRIsDEUv4LPoVPQdPVVWampqMxq/Zxpqk08MSRIsDCUv4LPoFs+D1paxYqpiPrSgKXV1d2O32hAnjB7prNBHpCKLe+snlclmdLiwGNJbwWeSUeIKnIwhC1NpeNhEEAUVRcLvdtLa24nA4kGUZRVFwOp04HI6o/1uilx7xBLG1tRVJkox7Gdv6yWazWYJoMSCwhM8i6+i98BIJnk6urKxIJEIoFGL37t0UFxczatQoRFE0JuhwOEwoFCIcDuPz+QiHw8Y5u93uKEGUJCnr59efpJP0n62x9PFiLcRQKARYgmix97GEzyJrxDZ/7S0lQRejbBGJRGhubqatrQ1RFBk0aBAVFRUAyLIMgCRJ5Ofnk5+fH7VvMBhk586dOBwOQqEQnZ2dhMNhBEGIayFaHRh6kkxkk7lM4wmiudOFJYgW2cYSPos+k2nh6GxZfGbBKy4uZvTo0bjd7rSsNT1Io6yszPid3qNPtxADgQDt7e3GWlasIDocDksQ0ySRIMqybDxL5io1elCNJYgWfcESPouM6WunhL4Kn1nwSkpKGD16NA6HI+r80jmXeL+z2+3Y7XYKCgqijivLsiGIXV1dtLa2IssyNpstriAeCJN0Nt2q8Vym+nOmj+N2uxkyZIgliBZpYwmfRdpkqzVQpsIXiURoamqivb09ruDpx06XVM9FEARD0AoLC6P2D4fDhiDq7tJIJILdbsfpdOJ0OolEIlEWTS7ZG2t8ucQcDdzZ2UlNTU0PQTQLoCWIFvGwhM8iZbLdCy9d4ZNlmebmZkPwxowZg91uz8qxs1UtRhe3oqIi4/eqqkYJoizLtLa20tLSYliGsQE11iSdnHiJ+frPvRX2tgTRwhI+i17JVfPXVMXJLHilpaVJBS/dY/cHoiiSl5dHXl4e0G2xFhQUUFBQkDDCNF5AzUCPMB2I1mU6nS7MUab9WRzdov+xhM8iIbrgpdr8NV16EydZlmlqaqKjoyNlwct0rL0hlIkiTM0BNX2NMO3Pa+rvsfrqbdBJRRDNVWosQdz3sYTPogd6Dt727dspKCiIinTMJonEJhwO09zc3CfBG0gWX7rYbDZsNhsul8v4XV8iTPtzoh5oFl+6JBLErq4udu3axciRIxOmXVjsO1jCZ2EQrzVQLsUj9vhmwSsrK2Ps2LHYbJk9opms8Q1kocw0wlRRFNra2sjPz895hOlAdHVmczwdsyCGw2EgvoWYqDyexd7HEj6LhL3w+kP49MCPpqYmOjs7+yx45mObz30gi1pf6C3CdMeOHQB4vV5CoVBUhKnZQrTb7fvUJL03hC+eVZfIQkwkiDabzQpeGgBYwncA01vzV1EUc1ZLUx+/ra0Nt9udNcHrK/09oeYKfT1QT8rXXcWxEaa6u1RRlB4Rpg6HIy2rZX+3+NLNUdWxBHHgYQnfAUiqzV9zZfGFw2E8Hg9erxeXy5UTwYs991SCW/ZXYl9mzBGmOslqmKYaYbo/C5+qqllZx0tHEEOhEAUFBZbLNAdYwncAYa6jmQq6KzJbhEIhmpqa8Hq9lJeXG5ZILqw887lrmkYgEIgKVU/E/mLxpUs2Ikz3pajOgTZePEHcsmULI0aMMCzB2LJtloWYOZbwHQDEtgZKFVEUjdqJfSFW8MaNG4ckSbjd7pz241NVlY6ODjwej1FAW1XVHtaLlTSemHQiTPWOGHl5eTmvYbo3LL7+fj5UVY16LvXnN9aTYRZGSxBTwxK+/ZhkvfBSoa8WXygUwuPx4PP5qKioMATPfPxcCJ+maQSDQbxeL4FAgJqaGvLy8oy8RN2dFwqF8Hq9hgWjaRrNzc1R61y5ClPvL+soFwKRKMJ0y5YtVFVVAeS8hulACW7J5XiQeAnCTGxzYOjZ+skSxGgs4dsP6avg6WTaNigYDNLU1GQI3uDBg+OuCWXblappGl6vF4/HQyQSIT8/n+HDhyMIguHi1TuEx7Ngtm7diiiK+P1+I+gjdsJ2Op19joDcXycfXRCdTmfSGqbZiDDd31ydsWSypmg+v9jWT/rnuhAe6IJoCd9+QqrNX9MhXWEyC15lZWVCwTMfPxuWj1nwAKqrq5FlmWAwmHJZK32yLSsrM845WdFp3WIxW4cHehBCInHQ1wN7q2Ha0dFBKBSKijA1/998f/cFIRoo46UqiLIsE4lEKC4u3u8F0RK+fZxcCJ5OqhZfMBjE4/HQ1dWVkuDp9FX4NE3D5/MZa3jV1dUUFRUhCAKtra1pHzteJGiiCdvsLvX7/YRCoR4RkPq/B3qNzb1FqhGm8e6v/swritIv93d/FNpYQfT5fIRCIZxOp/F5rIXo9XopKioyttlXsYRvH0VVVYLBIJIkZbVwtJneLD5d8Px+PxUVFQwZMiStSShT4TMLnqqqVFdXU1xcHHX9sfcim/dGFMVeIyCDwSCdnZ2EQiEkSYoSQ73Yd3+wL6YYpBJh6vV6kWWZLVu2xI0wzfYLh6KpSEL/Wnx7I5gmtltFrIW4a9cuhg4dagmfRf+iP4iyLLN+/XrGjx+fsy9IIosvEAjQ1NSE3++nsrKSoUOHZvR2mq7waZpGV1cXHo8HRVHiCl6mx850HzOJIiBjS4oFAgG6urpob2/vYR3uaxVUzORaZGPvryzLVFVVZVzDNBU+Ez0slbbSXtzJGK2UiylHJPd/n/4OpoHerUz9RXh/8GBYwrePEK81kLn5Zi6IFYJAIIDH4yEQCPRJ8BIdPxm6hReJRKiurqakpCTpdccee2+VLBOEniXF9uzZQ15eHvn5+UZ+XLz1LfOkvbcr2gw0zF6OTGqYphJhGiTC67ZNSIiICGy1eflA3MlMdVjOr6+/1xQB49lLhL6sYgmfRc5J1gtPLymWqwdRP75Z8Kqqqhg2bFjWqlj0FjzT1dWF2+1OWfD6ej79JZDm9UMz8SqohEIhQ0BjBbG3v8P+mlTe21jxXjj0/VKNMPU6FcJ2hXyh2/MhCSLtQqg/Lm+vCF9vc4n+XbWEzyJnJBM8nVwLnz75btu2LauCp5MseEZ3aerurNLS0rQm1f4UsWwSb33LnH8Y686z2WxxIyCTrXfmioEkfIlIJ8LUHwoSKffR7uh+QYugMiTsRJbknEfw7i3hSzamJXwWOUOf5FJp/prtPDgdv9+Px+Mx0gHGjRuXky9hPHHy+/243W7C4TDV1dVpC16iY6d6jP4MOkkVQRCM9a147jyzddjS0hJlvUC3mzgvL2+/SrfI9hpYogjTa5Qa3mQjzd52vhUspyoosD20HU3TEqZcZIO9UTrPEj6Lfsccng2pTdTZ7p6gC14oFKKqqorBgwezadOmnL15msUpduyysrI+f/EzCW7ZlzC788zo1ovuyjOXbUtUri0b7AsWX7pUSgVcyCR2eHdQUVGBq7I7sEYPqNHvczyXtPn/6d7jgWjx6XOTJXwWfSZea6BUybSySiy6WzEcDhtuRV1Uc9mWSBAEFEVh69athuDpY2fj2AcquvXidDpxu90MHToUIGm5tti1w3SjH/vbrby3K7ekUsM0GAzS0dFhRJjGa/uU6B7vjSCSVCy+2HSHfRVL+PYSvfXCS4W+WnyJBE8nl5GjgUCAPXv2EAqFKC8vp66uLqtvuHsjnSGdcfYGycq1mZPF29ra4kY/plKubX+z+HRSyavLZoSpqqpGD8X+IlXh2x+whK+fyYbg6WQqfObUgGSBI/q5ZXOSMUeIlpaWEolEqKioyMqxzZhFTC9pplf+2B9KMWlobBHa0YCRWmnGuWXmyTrq+L2Ua4tnHe5NCywbeAnzH7GRPGxMVwdH3dO+rClmEmEqCIKR3tIfOZ56bEFvrs79wc0JlvD1G9kUPJ10hM+c/N2b4JnJlhVkrvJSWVnJsGHDkGWZzs7OPh87EZqm0dnZidvtNu63ublqrHtvX4kE1dB4WVrHdqkTAahVC/luZHxWE6szKdemaRput7tfyrVlW/jaCfKYYzUhIqhofK41cZk8CYH0a4M2CC18ITZTgJ3jlBHYiC8mye7xrl27DMsvnRqmmaJfX28WnyV8FimRarfzTEhF+DIVPJ2+VmuIFTxz0nuuhEbTNGNC9ng8DBo0CJfLZbx0JEoNAGhubiY/Pz9rnRhywZdCMzslLw66/yZu0c9/BTdTtJqo7XJhFSUq1xYKhdi+fTsOhyNpuTb9nvbVZZbta/tA2kUYBQEBCYHNQjs7BS/DtGIgdTffF0ITL9rXISGgorFN7ORyk4Cmgm49FxYWRrlM9RzPeC8dfY0wTeX6LIvPolf0bue5rLmXTPj0epZNTU19Sv7ORmuiRFVeciF8ZjeuJEmMHj3aCKLRx0yUGrB9+3ZjQtcn7thKKtmOhMwEGTXKupMQiAgq7EVjVZIkRFGkrKzM+F2ytS273d6ncm3ZFL5tQgceulDRjPuqmf6dznifSh6kr/cTEdgitBMggov01uviCVGiGqaxQUuZRJimInyWxWeREEVR8Pv97Ny5k1GjRuXUWoiXxxdbwLmqqqpP1U7SzRU0N5/trVNDNoVPz/+TZZnq6mry8vLYvn17WpatKIo9gj/MlVRiJ5VYd2kuG9eaGa9VslLdjV+UERBwahIT1aqcj5suida2VFU18g8zLdeWDeHT0PiztJ5NUhsyChvENkarpQBMUKsYrBUaY6U6nkOT0NAMC8+OiD2BqzMZ6QSSJApaMgtibxGmqQpfspJm+xKW8GUJc/NXwHCd5RKzNZZKx4K+jpGMUChEU1MTXq83afPTl5NeAAAgAElEQVRZM9kQvkAggNvtJhQKRSW8h0Lpl5aKdz6JKqmYIyFjLZnYSMhsJ47bkbgkMpGPxUZAY6paQ16aFkW2SUeIRFHstVxbIsvFPFGne087CbHY1oBPCDNOrWCYWsQWqQMnNpzYmKhWMUQpYqpay8FaRY/1vVTGO0kZxTaxE4/gQ0Lk1MgY7KRvJfVV2HsreqC7TPVnV09r2bVrV4+XOf08sm3xKYrCrbfeypYtW5AkiXvuuYe6ujrj82XLlvHoo49is9mYN28e5557btbGtoSvj8Trdi5JUk7z33REUSQcDtPZ2UlTU1NWBU+nN3GKFbxx48al/OXoS+WZYDCI2+026ofGpkPkMlAlUSRkrCXT3t7eYw3G6XQalVX6ggOJmerQpNvsawnl6ZRrC4VC7N6928hXTFSuzTgOGk/Y1+AR/QgIbBY7GB+pNNySAE5sjNHKGK9VZnxthTj4qTyNFiFAgeagIMMXklylDiQqetDZ2UlHRwfFxcU9Ikw3b97Mm2++SW1tLRMmTOCwww5j6NChfRbBd999F4CXXnqJlStXcs899/D4448D3Z037rnnHl555RXy8/O54IILmDVrFlVV2fFsWMKXAXqV8kTNX/W1t1xOPJqmGWHmfr+fqqqqrAqeTqJ1xHA4jMfjyUjwdPRzTec+6a5UveltovqhmQpfX8QymSWjT9zBYJBgMGi0JYqXOD7Qgmn2Joksly1btlBVVWV8D+KVazPf14gNGoUuI8LShogiqBSqDrrEMAICNk3k2+qgHueQrnUpIVKtFfS+YRL6O2dO0zTsdjtFRUU9IkzLy8vxer2sW7eOf/7zn/zud7+jtbWVs88+m1tvvTXjMefMmcOxxx4LwO7du6ms/OaFY9OmTdTV1VFSUgLA1KlTWbVqFSeffHLG45mxhC8NdMGLRCJJ3R+5yH8zn4PX6zV60jmdTkaOHJmzyTJWQMLhME1NTXR2dlJeXp6R4JmPnep9MgttKl3e4wlfKhX9c0HsGozb7cZut1NYWBi3zqb+Rh4bTDOQBXFv1JbU75N5om5Ru/iHsIGQLDO1q4LqdgehUAhVU9GGBvDbBSRJRJRESjUnZ6jjWCk2oqIxTa2JG4SS7bqg8WjGzzJpG4qgMTlSjbgXEvTjXaMoilRUVHD22WcbYlReXo7P58vKco7NZuOGG25g6dKlPPTQQ8bvfT5f1N+1oKAAn8/X5/GMcbN2pP2YVDolxKJbStn6wug5aU1NTQBUV1ejaRrt7e05/YLo1xEOh2lubqajo4Py8nLGjh2blYK8vVlmsizj8Xj6LLTpRAvmGv356a3Oph4JmSz3MFcTcgSVDkIUYCcvhWliIFRSCSDzUN5qugQZ8mBjkY9rwlMZrRWjKArfj+TzZ6GBzkiQGq+DQ9w2dgrbGOV04nTmoTgCBJ1qjyClXF9biAjP2D9HFro9K5tsbRzlLGTcABC+2G307545WKmv3HfffSxYsIBzzz2Xf/zjH7hcLgoLC+nq6jK26erqihLCvmIJXxIyETwdSZJQFKXP4qALnsfjQRRFqqurKSoqQhAEvF5vztcSNU2jtbWVYDBIWVlZ1gRPJ5HwybJMc3Mz7e3tGY2biatzIFhU8boE9NaWKFFZsUwn7HaCvGr7Cp8gI2kCs5Q6DopZ9xoIxF5bg9BKpxBC+tqdqaCxRvQwRCkGSWCb088oKhmpljDLNhwKSalcWyYu+XTYJnTiF2QjCEYAtuVnz7pJhVR68WmaltXglldffRW3282PfvQj8vPzjfgIgNGjR7Nt2zba29txuVysWrWK+fPnZ21sS/ji0BfB0+lrHU1N0+jo6KCpqQlRFKmpqaGwsDDuWmIukGXZyMNzuVxZFzydWIGKRCI0NzfT1tZGaWlpxuPGHncgiFqmJIvQiy0rZk4L0KMf/X5/WrmHH0q7iAhat6UnwPu2XRwkJxe+vV00GqCcfFQwYigVVAq/dl0+bf+ctWITIgKfSh7CEZWTlFEplWvr6upClmU2btwYtRbbkRfhs/w28iUHs9XhCSu09EalFp2XF9EUSlRXgq1zg6IoSQOuctGS6IQTTuCmm27iwgsvJBKJcPPNN/PWW2/h9/s577zzuPHGG5k/fz6apjFv3jwGDeq5/poplvCZyIbg6WQqSqkInnmMbLvmzJZWaWkpJSUl5Ofn50T04BuBUhSF5uZmWltbKSkpYcyYMVkt0pvKfdpXSpbpJCp5pacFBAIBY+0wndxDGSXmZzUqNy0eA6E7Q51WzCxlGCukHYRRmKRWc7TSHR6/SWwzEtJtiHwpNnOSMirusWPvq8/no729ncGDBxvW4Q65jd9LawmHIiiaxkfqJi73TSDf+U2UaaoiUU4+cyIjWW7bhoLKQXI5k0LZcyWmwt7ovu5yuXjwwQcTfj579mxmz56d9Bh6zIXP52PdunUceeSRxrNRX1+fB4xoaGhYH7ufJXxkV/B0dFdnOueg902z2WzU1tZSUFCQ9DyyafFFIhGampoMwdOFZ8+ePTl3p7a0tNDe3k5RURGjR4/OSpLsviZi2URPC7DZbLS1tTFs2LCkuYfYJex5Dors3aXaRuYXs8vpwy5IKKiMUEtSKrnVn6kTiSzMaUot74k76RRDbBLa2C14GaoVk6/ZCAnffB8LtNRfqvTgFnO5tnekPeTbCsgHVFXDo4RojchUBIgq1xbPDR1vLe0IdQgzwoNR0QgFg7SILRndm0zZV5vQtrW18c4779DQ0MC7777LokWLjBQnYD5wKnBK7H4HtPDpgpdKt/N0SVWU9ACVpqYmbDYbgwcP7lXw0h0jGWbXYjxLK1cCoqqqMfGGQiFGjRrVIw2gL2SyLrM/iqV+7YlyD1ewjX+LO5AjMnXBAo5vrcXlDlNvF2lyhSmzuTiUMgLOQFIrpr9zBiG+0D5rW0u7GMKBjWYxyLO2tdwiH8HZcj3P27+gUwgzSHNxVmRcWuPFjmVDNKxgURSwiTaqSyuoKnEZ+2RSrk1CyGmZw0Sk2oR2ILYl8nq9bN++naKiIhYvXozX6yUYDAKcCDwfb58DUvj60vw1VXoTpb4IXqpjJKM3wdPpS5J5PFRVpa2tjaamJlwuFw6Hg0GDBmVV9HR0IVNVlebmZkKhUFTC80B7e+1vmvHzgX03eYKDPJuD5jyVxkKJ6epoRpiCaUKhEN5Ob5QVE+su7e8XhrCo8Dv7GvYIPio1Fz+QD6EQB14humKP/vPBWhV3ho/GT4RC7GkVjY4nCscrI/hCbKJJ9KMBM5ShVGnfrMv1pVybvl0kEsnZEkMq1xj7+UBMqamoqGD+/PlceOGFbN68mYMPPtg8p5+eaL8DSvgikQgdHR0UFhZmzaWZiESipAuex+PB4XAwZMiQqICFTMZIR7wjkQgtLS20trZSXFzcq2tRFMW0XLaJ0DTNEDyn08nw4cPJz89n06ZNOZs0NU2jubmZlpYWCgsLcTqdPToHmCfxXHecH2i0CyHMj42EiI/u3KxE9R/1STscDkdV99BfIlpbW3Oee6hpGn+r3MFusdviahL8PGX/lP+RD2WsVo6bnYhfd0cYo31TNFtCpIj03ejxvl/52FkgH8YGoY1CHNR93cWhN1Ip1+b1epFlma1bt8Yt15aLNJZ9tQmt/rfx+/3897//5YknnjBeOF555ZUXgb82NDT8KXa/A0L4dAsvEomwbds2xo8fn/M3l9g1PlVVDQvP4XAwdOjQjAVPJ50EcHPwSCqCZx4jG9GpHo8Hu93OsGHDoibTbFuU+phtbW1Ady3PkSNH4nA4kGU5ygVqfvPWK+B0dXXh9Xp7rM3si9Zhb89FnVaMS7Mb+WOKpjJOK0+4fbLcw7a2Nnw+H5FIhK6urrg1Ns1NazMljMJmrZVdDj8i3c+RgMAeoTvn6wfyIRRodnYJPoZohZwTOSjjsczXF+8+2pE4OEtpHuZybfqzWVVVlTSNJV7nhUzntX21Ca1uif7pT3/i3//+NxdccAGSJBEKhXjllVc+ArbF22+/Fr5ELs3+eHsRRRFZlnMieGZ04Uh0PYqi0NLSQktLS0bBI5lGjprzDyVJYvDgwXGTXrMZmWoWWX2CHTJkCDabrccY5klcj4h0u93YbDZcLtc3Lj5vfBdfX3r1DZS1RAcS35Mn8J60gwgak9VBDE3RcjEjiqKxTlVdXQ2klntonrRTuZdtBHjW/jmdUpCNopciQaX26w4K1V+7GSVEzo8cnPY1JCNe5ZZVQiOf2jzUqoWcrIxKy3XaG+YyiMkKTZuLeScr19ZbkXQ9WGhfbEJrfpE9++yzOfXUU43Prr/++kdI0Kxrvxc+3ZrQb1C2K6okQhAEAoEAGzZswOl09rB0skUil2qs4GUaPJKuRWYuqQYkTcfQj99XEdDHdLvdiKJoiOy6devSOo4gCHEbrcazDsPhsFFebCD16kuXUvL4jjK2z8eJtS5TzT3c421hRWgngqJxbGgYBY68hPdyubSdsKBi0yQmBsrYWBCiSHVQrbm4RJ6Y8rluEtr4k20dIUFlolrFmZFxvaZqmOeLZdI2HravQhU0FDQ2R9q4Sp6W8vi90ZsIxXtpg+iqP+FwOG6R9HheDH0+TCaOA9Xi0ykoKGDlypUMHTqUgw46iIKCAhoaGhKu0ezXwhfvD5mtiiqJ0N0+Ho8HQRCoq6vLieDpxFpM2RK8RMdPhKZFt0UaNGiQUWEmGX0VPp/Ph9vtRtO0HmNmcux42yebaKICQLxewuGwsY4TuzYz0AIDskmq68zmHDmxyMm9js9oFv1omsZGOczPWicTDoWNe2ne3lfqJ+LsTjlyYGOaUsb14cONTvSpEEbhMcdqfIIMwE6xkzItj1nK8IT7qKoaFfi1TNqKKnQ/JxICH0i7uFKemjWrL3a8VIlX9Qeii6Tra7Pme6vPhcFgMKErWlXVfgu0yYSWlhbee+89li1bRldXF4IgEA6HVeC0hoaG12O3H7hXkiPSza9LFXO0Yn5+PlVVVUbVk1yiW3yKotDa2kpzczOFhYVZSw9IxeLr6urC7XYTiUQYNGhQWl0iMhW+2Maz8ZrtpnvsTKrzJOvVF+uG0oVQD2nP9Vv03igcnQ7vSNto/rpNkCAI7HL4aajwM1PpbrcUey9Ht7tY69oDqoqCxogWEZ/ckbJLD6BVCNAk+Mn/uqKLhMgWoZ1ZJBY+8338XPCwQWinGT/l5CMiYEfKuqszmx6pRIFK+r31+/0A7Nmzp0e5NkmSaGtrQ5KkHoLaF2RZ5uabb2bXrl2Ew2GuuOIKjjvuOOPzp59+mldeeYXy8u4154ULFzJqVM+CA/r8d+2113LttddGfVZfX18EyPHG36+FL1G1k2wKn56P1tzcTH5+vhGt2NXVRWdnZ9bGSYQgCLS1tdHZ2UlBQQEjR47M6gOazOLTxSccDkc1gU2HdMVJ78MXDAapqqqirKwsp27UdDHny8WGsZutQ7/fz5YtW3pUU9lXrcNkIhsigoTIV0IL79q2E0alXi2L6oUH3YsxqqawTNyKImiMVyoZbC8y7uURVDBUqOXT8G6UVh+z8scSVqJdek6nE7vDztqiDlryZA4SKpkk1BhjlGl5lGp5RjK7goqiafxd2sBgrYgpag2x6Gtun4oe/mJbTw0FbBBa2ImXkWoJ58l9D6CJHa8/lmL051SSJILBIHV1dT1c0WvXruXee++lra2Nuro6JkyYwLhx4zj88MMZP358xuMvWbKE0tJSfvnLX9LW1saZZ54ZJXxffPEF9913HxMmTOj1WKIo4vF4ePXVV1mxYgX3338/b7/9NkBRQ0PDnnj77NfCF49sNYlNJHg6uayjaR4/GAwiCAIjRozIquDpxBOPQCCAx+MhEAhQXV2dVHwyOX48Uu3D1xdyKZRm61CWZSRJoqysrFfrMLaT+0Al3n1T0fiHtIldghdVUNkktDOEblfxWqmZmZGhDNWK2CF40dA4SC1ng9hOl9DdH+8LsZnz5G9RyzcvEHVaMRVhidZwKyWDSqLG0116i8X1vGfbiRZReVNp4KTOYRwZGWLcx0sj4/lr/iaCQgSXZuMrqYVNQjsKKo0RH6cqY3pcmyiKrBWbsCFSoeVxcmQ0bqGL28JHMFwrzeq97O8EdnNEZ2y5tqOOOoqjjjqKL7/8Er/fT3NzM1999RXvvvtun4TvpJNO4sQTTzR+jvV8fPHFF/z+97+nqamJY489lh/96Edxj6O/cN13332MHz+elpYWbDYbzc3NAI/X19f/oKGhoSN2v4H7TcoCydb4MsUseC6Xq4fg6WTbskw0vsvlory8PCeiB9ECHgwG8Xg8RuPbbIhPb2JjbktUUVHRax++ZMceaFZUMuswtjVRvFqbA806jD2P1eIemsQu8pBoR2aL2E616sKOhA2RTiHM7aGZfCTtxqYJDNdK+KPtC6MNkoTAOqmZWiU6GjiRdam79NY7fBQK3wTUbC2IcHLHN53FS5pDXBSpweFw8PygTchOGVXqjlpcJTbGFT5BEHBqklGtxYlEnVacURRsb/QW3JJtUonYzMvLY/To0VHNYvuCHvDk8/m45ppr+OlPfxr1+amnnsp3v/tdCgsLueqqq3j33XeZNWtWj+Poz8H69ev51a9+xVtvvYXL5eK6667j97///SiI74Per4UvHpkKkr6G1tLSgsvl6tXCypZlqRO7hqgL7o4dO3JqWeprfDt27MDn81FZWcnQoUOz9sVMJHzmyjKZtkPKZI1vIKQZJGpNZLYOY0tgxYss7e8yYrFj+YkYhaELceDQJGRU7EhENJXhajF2JI5ShgHQQTBqfxUNh9ZzQo43VjtBtgodDNOKsCMSMH2WJ8XvLB4KhXDadqCqQWRZ7q7Xq9rY0boj6l7qFtFJyih2iV72CF04kTg1MtpogZRN+jtZPN1efNmisbGRK6+8ku9+97t85zvfMX6vaRrf//73jb/XMcccw5dffhlX+HRqamrYuHEjXV1d2O12vWmtHYjb3+mAE750BckcNFJQUJCySzFbrs5EgpftceKhdz2XZRmn05mWtZUqscEz5qjUvnZpGChClg0ysQ5tNptRrSgbyePJUNH4j8ODJPkYrBYyXqtilFpCg9iCTRCxIXK0MpxSHETQOFit4FtaJe+IW9kktWHXJI5XRnCYMoSV0m40NGq1Qg5Xh/QYK1b4vhJaedm+DgUVURMYrZayWnSjCCr52DklMrrHMXTX8+nCwTzr+IwwKoIGc0NjKafcWIfV1w8bGxvJy8vju86RhJxQ4iig0J6XwJ7o470cYMKnl/3L5ne/ubmZSy+9lF/84hfMmDEj6jOfz8dpp53G66+/jsvlYuXKlcybNy/p8S699FIWLVpEKBTivvvuY/ny5QAPNjQ0ROJtv18LXyJ3SCgUirN1NOYJuLCwMO2gEX3SzfSt25z47nQ6E6ZF5Ko1UVNTEx0dHZSWlhoNcHOBuZ5ma2srTU1NWe3SkO657Gsly5I1rvV6vbS2tkY1WI21Dh0OR0rRkIlQUHELXSzN20yHFKZYVNgmdiArKhvEVj6QdtAphJmi1nCtPB0X37zEfCzuZrXNjQ0BTZD5k7Ceq+WpTFEHEUahjLy40ZKx36nlUndxDgkRBGjCz63hI9gt+BihlVCYpEzZOK2c60Mz2CK0M0wrokJwQQFRuYebN2+mqqrKCPwQOkI0hTrZoyg91mF1a3uV2Mirtq+QUZmm1nJ2pD7lyM/+XuNLRfggu50Zfve739HZ2cljjz3GY489BsA555xDIBDgvPPO49prr+V73/seDoeDGTNmcMwxxyQ93pFHHkldXR0ffvghmqZx0UUXMXr06CcSbb9fCx/0fOvvzdXZV8Ezj5tJzmBsTcveEt9z2Zpo7NixiKJIa2trVo6fCL/fz1dffYXL5cpqVKr5bz9Q1sF0cmmJ6tZeXl6e0eIKeiY4t7W1RUVDpltaLIjMn20NdAohVhRsZXikmPEUYUNkubiNr6Q2CnFSqDnZJfjYKnRElfjyCH5sCPiQWSO68QohAshcIk9kMEVxx/xU8PD3wnWoToUTRJXVopvl0jZcgp1RanddTgWNcvIp13quvcejBCff1pI3Oc3Ly+vheTDX1zTncfokmUeGrUNQJSRR5A1pI4OEfI7WEqdMmOnvNT69QHayzyG7wnfrrbdy6623Jvx87ty5zJ07N6VjKYrCZ599xmeffWZUAfr000855ZRTTm1oaPhHvH32e+GLJZGrM9uJ35CeKMUWr0610ks2gmiSdWrQrdZsrxfp5cVaW1sRRTEnif5m4dOtyt4qYvSHa7Q/Rdg8VjLr0JxmEWsdxkaW6sf8SGwkKERwIJGnSuyw+xiLggOJLkGOSlcQgUbRx8HKN8JXrblYRwtfis2EBAWnZkMWNJbYNvLjyOQe17JVaOe3jk8IiCEUm8LrzneYrg4mDzubhHYcgkStVsgUJXuduvV7lMh7FC+P8zN1D5pDQlS6v1tKSGF1xybqfFrUi0W83MNUntNsszea0GYD/e+yZcsWrrvuOg477DAjavrr3MQjgQNT+GIns9ioTnPx5mwJnk4qwmduT2S329Ou5anXBM0Es9gnKlydTiHsVIgtL1ZS0h2SnqtEf1VVjfGyXXOzNzYJbTSIrQxRC5nUi0Wxt+ittJguiPpal08Is6zSQ5dTod0eYZhaTL7NyQi5hAaxDb8WoRAHsqDwvrQTCYGDlEoqcTFJiXaXT1cH0xkJs9q+hzxNYqRWiohAlxCOe67/kfagCCpoGhFBwyeGaNb8DNOKyVNtlGt5nCMfxHitKqv3KB0hEgSBMVIlJTYXsr37u69qGkfY66kprDDuaWzuoblmqX6c/mJfbUKr097eztSpU7nnnnuifv/3v//9jkT77PfCF4sufOb2PNkWPJ1kwmcuqGyz2TJuT5SJq1NVVVpaWmhubk7p2rNlCcUrL9bW1kYgEOh95wxQVTVqvLy8vB41N/VeaObrDwQCfW798rG4mz/avwC6XW+nREZxSkyY/EDGnDph5klxDW2aihrRCKlhVio7GN9WikPQOMZXzXcYxtvFu/igcBeFmoN2IcQWsZ1bgjOopOfLzRx1BF2RMKslNxIiCirD1ZIe2wFUawUodCe62xCxaRIFmh0FlRIczFKGZ130MvF2FGDnJ+HJvGrbgCwoTFcGM50hkEcPN34kEjHEMBgM0tHRnXK2efPmuH0PcyGIqTSh1evYDiT0e6HnEr/++utMnz7dSPNqaGgI19fXCw0NDT0mrwNO+PQ32Q0bNqTVnicT4rkhdcFramoyuhak24DWTDoBGbE5gKmup+ljZPrGl6y8WC7ci3q+YTAYpKysjNraWr12X9yam7qrT38L93g8PboIxHP1JWOFtMP4t4TAe9LOfhe+XKQztEthHIID7JBHHkO0co50DKWrpYNDQhU4bBINkWZCoTACGhWCHUEAZ0eEgD3+C8V3lLHka3bcoo8qrYDjlRFxx56pDKVBbOUddSM2QeRYeRirJQ9hIcJotYz/int4y7aZKrWAy+SJVMQR2nTJdI34IK2SG+Xec950a1v3eMiyzPbt2xk6dGiPDiGJChv0te9hqk1oBxrmXnxbtmzhhRdewGazoSgKsiyjadpTDQ0N8+Ptu98Ln/5AmNexNE3rl6hB83qiuU2PKIrU1tb2SfB0Uonq1CNEPR5P3JSIbIwRD728WLIKL9kUPj39wuv1UllZiaZpKd1jPfFZLzBdU1OT0NWXaiBI7IhiLuLe9wIVWj47hU4EBDQ0RqhlHKXV4ZbdOPOdlJaWMt42hNX21u5tVI0y2Yndr+IJxX+hcDgcHG8fgaB236PFtgbeF3eSL9iYH57IyK+byQoIXCZP4pSmGhSbxi9rPuMQrQo0WCs20SYEGKGVsl3s4Gn75yyQD+vz9fY10CSAjIBgJOX3hi4yyYqi689ltvoe7qvCp5/XihUrGD16NP/7v/8b9dm3vvWtqxLtu98LXyQSwe1209bWRnFxMaNGjWLDhg0Z54alg27x6S5NfVJN1qYnkzGSuVN1wUuWEtEb6Yb561aTz+frtcJLNoTPHI1aVlbGuHHjkCSJ7du3pzVWbFudeK4+cxJ5sjSB4/PreCb/SxQUNOD4yMg+XWN/00mIgBChUsuPStI+J3IQf7V9RYsQoFpzcaoyijekzbhdzUyhllJKuTAynlYhwGeShwIc/Fj7NkNrul2Q5heKcDhMR0cHoVAIVVVxOBysLG3h8YovQBQQBZEtzg7+L3gKTtNUZdMEukQFPxHysOEVwrQJQUTtG+9Fi5Ad93mmVrOGxnO2tXwiNSIARyt1KTXFTSZCiYqixwYnpdv3MJUmtAPNzQnffF8LCwtpb2+no6MDQRBwuVzYbDYaGhoSPgT7vfC1t7ejqmqUhacLUi5rH+qVNvTO49kWPJ14whe7ftjX5repWnzm/L9Uy4v1RfhUVaW5uTlhsnsu3KjxAkHiJZEXhEKcZ6thZ1GQ0UI5o22lhJyhfs9NTIVWAjxnX0uT4GeQVsAhShUNYiuCACWakzMj4wzhKcTBxZHuwsEqGo/ZV+MRuujK87PO1sGPhCJGaqX8j3xo3Lr4iV4o9Mm7wbkZRVNRZRVN1dgk+lnj2cQYscLYT1VVyrU8hmhFrJIa2Sl46RRCyILS3VUeO0O0nk2PMyHdCMsAMk/Y/8sqqZGtQgfj1SrysbFc2sZ4pbLXju3pjpdKcFI4HO6xnm12me6rFp/+3a6urub5559nzZo1DB8+HFEU8Xq9LF269OiGhoZ/xdt3vxe+qqoqIpHo5P1slxMzY27EGolEKCgoYOjQoTmL0jILX7yGrNlwp/YmIH0pL5aJOGmaZiS7FxQUJAzO6a+SZYnSBIZHIkyIU4BaFEVsNpvRty9XzWtTtVb+YF/LNrG7k8hXQisfi7uZo44AICDI/EfcwxHq0B77tRFgh9CJikqjzYdLdLBGdDNSSb9os3As1P8AACAASURBVO5uHm2rYpljN3YE0CBftTHCVQVB1Zi8I5EI9oCd88LDWD5kC8WigxEU45NkOgWZI5VhXCRnXkDZTLoW3zP2z1krNdEmBOkSZL4Um5mq1mBDpFn0Qy+ZR9lKXk/2ghGbewiwZcuWuNHOupGQzeezt5ZEy5Yt49FHH8VmszFv3jzOPffcuMfRz6m8vJybbroJh8OBz+czclSXLl26I+6OHADCF49c9OQzCx50v4UEg8GcV2HQH0xd8KD3rueZjNFb7mOm5cXSERuzJetwOFJaq9xbJcuSlRjTX4rMTUHNIpjrNItYzG5BFS0qnUBAIEL8l0QnNnxfF58O28IIYohPcXOWUp/xuZwXOZitQierbI3kazYujUykprAcU4MGdu/ejcPhwCXB8HCR0Y+yQLAzIVzGmYGhRJxBwk6tz/cw3e9vk9DdX7Bcy6dR6CJI90t3HjYmpZBfmOvk9djcw0gkwtatW6mrq+tRB3bnzp089NBDDB48mLFjx3L44YdTX19PTU1Nn+5pspZEsixzzz338Morr5Cfn88FF1zArFmzqKrqGa27YcMGRFFk+fLlHHXUUUyePBlZlnG5XDz//PMACSeH/V744v2Bstk5IbbzeHV1tdGIVZblHtZmtgkEAkQiEfbs2RM1djaJFadslhdLNddRT4UQBIHBgwdHiUmy806H/khg1609m81mVLrXNC1pmkXsf9maGP8r7uF9aRcbhFZkVIZRgh2RoVox7YRoF4IUaXZOVns2AIVut2e15mKj0EYElVItH6dgI4zCdqGTT6RGhmlFzPy6CHVK9weBm+QZCdqHfoPeNudo+0g+knYjaQKoGnMi3efa2dlJOByOioSMLSuWCukK0WCtkG10koeNerUMBfi2MogTlBGU0Hu61N5KXo/3kjZs2DCKi4tZvXo1e/bs4bnnnuOrr75izJgxPPPMMxmPmawl0aZNm6irqzPye6dOncqqVas4+eSTexxHr9ayePFiPv30U2pqalAUhfz8fFasWAEkvuH7vfDFIxuuzmSCp5Or1kTQ3fVcD7sHGDNmTM7dqXo5NY/Hk7XyYr2Jjd/vZ8+ePf3a3b2/0aPyEqVZmHO80kmzSHbtu+nkN/ZPCKPSSZhOMURxxMHR1DEpUs0fbJ9/nTOnsVv0MUiN/6IxRR2EMyLR2tVOhbMIydldp/KXjpUEhO6XvnlyPZdHvt33G2W6Lv1a58uTGKuW0yoEmKwMYkR+adR7fjzXXrxCBony5NJ1dX5fPgQVje2Cl/FaBT+QJ1JK6t+RgVSgWpIkJk6cSGlpKeXl5Ubpu77OaclaEvl8vqjvQEFBgd5poQdz5sxh0qRJhEIhjjvuOPLz8/H7/Ua9z7PPPnttonPY74Uv2z35NE0zREdRlKRWVi7WEgOBgFGFRM+J+/LLL7M6Rjy6urpwu93Y7faslhdLJE6ppEJkeuxsbZ8pqY6jr3uZ73U6aRaJJm0VjYWO91knteAVwiiooEHIpnCwXMXHUiPDhK+TyAX4SNrN5DidyQFmKEPYKnYQ0uwomsbhkVr+YdtkiB7Am/bNzI9MylpKh/m6BASOUeoSbpuorJhuYevu5tg8Of2/SCSS1nNnR+KHcs9ya6kykITPvI3ZKsvGel+ilkSFhYV0dXUZP3d1dUUJoZmSkhJKSkr4xS9+gcfjYcSIEWzatIktW7ZQX19PQ0NDQr/Bfi988cjUEtMtvEgk0iMRO9E42RI+sxBUVVVRV1cX1TU5F8nK+rplV1cXoigyZMiQlFyM6RArAuZcvGw1u92fSCfNIhwOIwgCjY2NUZP5Px2b2SV6CaHQSRgNDQmBaiQ+tXko0uyM1cq/GTPJ+VTiYr48ifd96xlONePEQbzN1uhz1lLtS5AafX3WzRa2GT1PLtY6BNi5c2e/NADeGwWqexsv28EtyVoSjR49mm3bttHe3o7L5WLVqlXMnx83B914Du69914CgQA//vGPmT9/PsOGDeODDz7gj3/8o+2AbEuUiHQtMd3Ck2WZqqoqSktLU3rosyF8ek5cV1cXlZWVcYVAHyebXxh9TU1VVfLz8yksLMy66ME3wmfOxSsvLzdy8bJx7HTYF1yj8YgX0u71emlra8PlckUlPH9WtQ2pWKHc5qDNGSQkKJRo+TiRkBCoVF2oaIiCQETTmKLEt/Z0XNg5KFhKRUH3m/k8uZ6PxF3slnyImsDZSuoteVIhVw124+XJ6SX1iouLe0Tnxksa72uKlKqq/ZJjbB6vt+9ZttMZemtJdOONNzJ//nw0TWPevHkMGhQ/KEh/BlatWsXrr7/Ob37zG8466yyuueYazjrrLBKJHhwAwpfIBZlKYWfzOlp1dXXKgmceJ1OXamwVkmQ5cdm0LOOVF9PrXeYCPQF3w4YNfW48G0smrs79DUmSjEAB6L7fNaKXqaqD/2puasP5NIkBBDVCs+CjLGSjTq5mljqSPflBhgrFDNfi1870EeYz0UOeZqOSb+5zrVZAvVpOCU6KNSddyGwU2hjzdQWWvpIr4UuEzWbr8eJntg7D4TA+n8+oohKvsk+q5zvQXJ16rdL+bEk0e/ZsZs+endKx9JeS9evXs3z5cu699146OjoIBoNJ99vvhQ/S78mnT/6ZCp55nHQFKRwO09TURGdnZ8qWTzaEL9maWi7WvvTO8h6Pxyghl+0i4fp564FIbW1tRmCI3qsunYCQfQkfYb6UWhDtYcw9zAVB4DhG8ZZjM8dSQLFmxxYR+Jt9I5qqkmeTCATDiM1+1uft4ENHmCoKOCkyEpcz30iz6BRC/J/9MyKChoKKq1jmMqE7SnWD1IZLsOPCToPYyjpBQ0Lk/4WPyYrl15/Cl2isRFVUzC5nn89Ha2tr3Mo+ekBSLANN+PR5JZfFPvqCzWbjhBNO4Pbbb2fKlCmMHj2a8847j9NOOy35fv10fgOKRK5Ov9+Px+MhFAoZLs2+PITpCJIsyzQ3Nxtlt9JJAu+L8IXDYdxud9LyYumWLEuGnovndruNRrvbt2/PuujpyLLM1q1bkWWZsrIyZFmms7OTpqYmVFXtEQySbeELIBtdGi6Ux5NP7t1YLfh5zb6RgDOIXwsQFl0colbxudiEiMBUtYb58iSCKLiw8QfbF8zQhnYv5tkgWKLyn6IAuyXQFDsbFT//8G/lxLYhRprFJ+VttBd5sUkSks3GNrsXt+BnJC5GqqWENIV1Ugsa3ZGhG8VW3hN3cLSaOBAlVfpT+NLJ40uWuxlb2SeedehwOPq9PFhvbsxcNKHNJna7nUsvvZQ5c+YwePBgIpEI999/P2PHjk263wEhfL315DNHSmZD8HTMaQCJvjzmqid61/N0364yEaZ0yotlIy3j/7P35mFyXdW592/vc06NPc8ttdSSWrNsy5Y8zwM22OAJAzbgCBKmELi5gS+X6xvyBJz4Evg+QiABfC/GkBguxMbgiwOEYFvCsyVsDbbmsVtq9Vjd1V1znWl/f1SfUnV1dfUoRXnE0qM/urvq7FOnztnvXmuv932LuXheo4x3feY7PJkm0zRpaWmhtrYWx3HGXafihhCPE9nZ2UkgEJgV76sw0ljcF/w527U+AJ7WD/N4+s55+4yTxQ4tJ6IgEGhI3pC9vKUN5IWlD8phPmivIzwGwpXKR4QUCsUeEWFYpnlO6ySIwQpRR7XmJ24IFoZyuaPjOBxQh9BECtu2yWSzuI7L4OAAfsMk4PdzQXUtr1acRBeCRlVBHSF6ZIJJuPAzijMBfC6KR4yd7KjrJozBx8TFrJ5CbmyymEzZx7v/TNPMk8a9hWip++90fGbHccpuLZztXnzZbJYXX3yRRx99lEWLFvHJT36SH/3oR3zhC19gMksiOEeArzg84Eun0wwMDJTslJyP8DysSq2qCg1w57q3NZOMbzbyYnPN+DwunuM4eR++4jLqfE1mhU0ywWCQUChEXV1dydcWN4R490Nzc/O4clU2m52VssoTxn62a3358t4OrY/HjX3cPq74eDrilOu8ENAnkrRRiYZAjBm9HhUjrBzr3HyHs5SfiQNsl/0MiQytbpjXtF5MaTPqZlnl1nNxAZ1B0zSuZxlHjQQZYePgsqDPZm394vze1zX9DbzkVJIVDlITpGWatrSPjMzMyDmg5Kc7A8D3lH6Al7VuLGmSkDYPGzv4WvamcYLdc4nC7LAwjh07Rn19PUqpeXVgmCym675+tnVWe/fAzp07+eUvf8nb3/529u3bx8KFCxkaGuLrX/86k4EenKPA53F4urq6TnvLfDHwFcp8zVX1pHCMqbKmuciLzYctUXNzc8m90vmawIoFq1esWJFX/vdiOp9BCFFydV5KWaW4VFo8GWmqxN6QktM+l9nGBU4Tx0UMF0VaOSxVNdji1MLFAfycmuyCGHzQPo9mGeZVvYe3xAANBOlXSSxcBkWSO+3xpaMKfHzCupDdIkIInWAsgdFq5EvWjTTyF6KKn8kDZJwsFybr6YiF6cv2TbrnNd2s5kwAXx9JNGROQEYIhkWGOOaMyOizCaUUwWBw3LNZyoGhlCuIB4zT9YyE6ZnQSinPusYv7x7o6elh0aJFXHbZZezevRufz8ctt9zCiy++WPb95wTweV+aZ1DqESRXrlx52lcyHvAVynxVVFTMq+N7uYxvPsadacY3Uy6ed/zZlFOK1WTKfb7p2BKV+vtUyiqZTKbkZPS2QAtPNbey1ehFCLjCXsi99mqSIjbjzzmTMNCwlMtLvpNUoVFHNVXKx7DI4AIr3dqSnZqrVD2vqJMoAT402lU1lzmthJRR0j3dj85GlcsEj6n4hMlxharjvztjPK3w2H/Gk/AzmUw+q3kjPMTe6jghzcd7zFW0+WrmRU1lNrFEVbOZLoa0JH5N5zy3mappSI7NNUoB0WQODK7rjluQzdQzcrLxiv9+NpY5ve+/srISpRRbtmzJCz0cPHhwXCdzqTgngC+TydDb20sqlaKhoYGFCxeyb9++M7KKEUIQjUbzhMz5kPkqjsmsiTxACAaDcxp3uhnfbLl4s8koC50odF0vqSYzFdC9JQfZKfvREFzhLKR1hqv5UsoqxY0M/++Ri/ll6BhCwLvSy4j7R7Asa9aiA3vEIC/JE7gC7rfXUVk0GQ+S4sfGHrZox7FtG6W5DMsMluvgUxqV+LhpEofzRaqK99trCWuH2EEfy1QdOrIkjy9CiseM3QyJFC2qgutF5bQ/S2FTR1VVFQBviF5+o+8fWyQm+Jr8Hf+1ew3CdieU+M4E8K10a7FxSQkbG6hTgTNiJTyTz1ZYfi+M4r3rUv583v+pmmnOduB729veRjQa5ZFHHqGhoYF3vvOddHR08PnPf77s+88Z4AsGg7S1tY1TOzmdX6oHPN7G9Uxdz2cSxdZEnoPBfMmLTZXxzdWlYaZ0CU8+zXGcsk4U5Y7bJUZ5RjuKPlby+7l+kPvFmjmXIIsbGZqA/6IW51bmxilVEMuySCQS016ZA+wQvfwP//MMywy6kryoneA72Vup4FSpfIfWj40igUlMy+AIhxoRZlTLcp5qoo8EX/C9yG32Mi5yW8a9F3JZ2v9jX0avHeeIHKFRhVmlJu6R/sDYTbfMWdocFSPEakdYTWkx6+nEbi2C1DTk2PMY99sEljXQ5lROUFOByW10hBA8ox1jt4wQQON91mqqCbBD9mMoyQWqaVrSaVu1XparWlrTOdDtM5LEMKclND3bmK+97qn8+QqzQ8dx6OvrIxgMjltgePfgfKu2eLFr1y6++tWv8oMf/GDc77///e/z5JNP5vflH3zwQZYtm3hfeddp//79XHDBBXz3u9+lp6eHBQsWsHDhwinP+ZwAvtra2kk9+eb7S/VczwcHBzEMg2AwSENDw2kDPTjVdRmLxRgYGJiRg8F0j18KEDwu3lS+eFPFdIHP2zPMZDLT5ldOdtwTIp4HvbGz4LgWo3QbzNyiuFTqfV91dXXTcnMPBAJomsaP9H10yRiWcNEQZKXDv2lHeK+zJj+WBJKY9MskWWXjCJetopernDZcFG/JCFksmrUwe7QIf2Cdn+/ufEGe4E1tgKDSudteydXu5K4KQ+IUQVggGNUyc5qwawngjEmnQc7Gp1YFx/Hltsoevuh7lX4rxhVyEQ8NX4GTtcY5MeysHeWXdSfwaQZSk5wwYtTgp1cmUcAKt5ZPWhumBD8NiRr7hwANgTFPjS2ThVd2PB3Z7GRSdwcPHqSuri7XnVsghJ7NZnnsscdoampi+fLlVFRUsHDhwnk5t0ceeYSnn3665Jy4Z88evvKVr3DeeeeVPYYHfM899xzPP/8873rXu7j22munBXpwjgBfqfA6O+dLJUQplQceTdPywHPixInTZnrrhWVZxONx4vH4hK7J+YhiYCrm4s01m50K+CzLYmBggFgsNqls22THnSwaVRALNz+ZuShaVQUZNTzzDzDLmE6p1Nv/klLy5tIehoycd54tXGJk+Y1+lA1uC29pg/xKO4KNS79IUqX8nFRZNAUWDn6hMUwaSzhoSkND4gB75CCXugvYJnv4qbEfOXY9Tso4f2leNSlALFQVHBTDCAQuiiYzgAjO/p57p91BlxhltzaIT+m811pNZUE26uDyZ4Fn2aNFwIDD7KW1vpK/NK869RrH4RntDXzCwHYcXNNkmzbIQjtMSOaaZ/ZqA2x3e9goFpS9P26zOzggh4mqGJpQvMNeQug0czDPNHnde+aKKyZKKTKZDFdffTVvvfUWv/71r3nkkUdIJpNcffXVfOMb35jTuIsXL+Yf//Ef+dznPjfhb3v27OE73/kOg4ODXH/99XziE58oeQxvMf6pT32KO+64g8cff5w//dM/ZenSpXzwgx/k4osvLnstzwngm0x5YT4sgwoNaIUQE0pvp8P01gtPYSaTyRAIBFiyZMlp0zC0LJeTJxVKJclk+sZx8eYakwFfIeWjtrZ2xvqd5QB1lapn0EnzljaABK53FlOvgpyc7Ycoit1ikB4ZZ6FbyTo10URTodgp+okLkwvd5vwkH5cmB0LD1AeD/Lt2gm4Zp9EN8tH0+eiGgaEEaZG7n1zXpWJE8b/019gTGsFEMaRliAsT3YUmFUJ3BMqQHFUj9MsEIyLLQreSnbKfMAZXODmrmf1yKA96AL0iQZQM9ZN4eX7YOp8f6/sYEmlaVJiLh31Qvp+gbGhI/ot1MY7lIsdoF4UxQpYTRU1BnXJ0/DE0jWa9Cl2L4hu7njVAjVMBTs6s1sxanIj0UJNMly0zV+PnL8wreD6ymxUNbSzx1c/+w00z/qNUW0p1WgeDQe644w42btxIMBikvb0936sw13j7299Od3d3yb+9853v5AMf+AAVFRV8+tOfZsuWLdxwww0lX+udd2trK3/yJ3/Cvffeyze/+U02bdrED37wAy655JJJz+GcAL5SMVfLoEI/PqUUTU1NJTOt+dTR9MLrTk2lUnk9zWQyedo2/LNZwfe/X8fQUBJQvPOdC7nrrsC8jVcMUIVyZnOhfEyVSV7ttnG125b/2cScF5rBb2UXm/Xj6Egc7SQ32RmuLSgbKhQ/qzhCl5FBE5Ln1XH+xNxAWtj8s/EWFi5bRa67cgGVdMkRXqrsJoVNWPgRykQKQZMI0hSs4YCIYLoOB7UR0tigciVPwxWEMGh3qhiUKZaqWnYzwBEtCgoaVZg+ketwriGQE6YeA5wwxriMqziCGPyRfUH+50Pq0LzcD5Px5OoIsMKt5Q3ZD4BQsNaZSCi/015Jn0hyUA4TVAb/zb6UX+udxLQsOgaL3BDvat2AdMhn1el0Ot8AUlxmXpauoFXlFndZbL5r7OKkSNBAgI+aF85rp+eZ1iCdriWRx/Otra2ltnZ+9FZLhVKKD33oQ/nO6euuu469e/eWBD7vWm3ZsoW9e/eyfft2ent7ue6663j66afp6OgoO9Y5AXyTCVXPNhPzmiumY446n8BXTBPwmnVGR0dPGy8sk8nwk5/EGR7Wqa724/cHePlleNvbHCaxyZpxFJLYC0uo890BO9WkMl+Tzk5tAH1sAteQbNf6xgHfqMyy2zdMrchdwKxweF47QVpY2CgEgrjIkpYOFa6PPpFAAZc7C0nK42gIAkrnfLeJtwIRKl0/w9oQSc0ey5QUhiuptn1sTNTTr6dx/RaWyuLzS6qkjyrXx3rVmFNTcXKlvR6R4KAcwq903mOvxsf0s+vTrXEqEDyceQdfNF6g34pxrbGMP7Mmruh1JJ+yNqLGriPAeWYTL2vdaEiudRZhoIHGpF6Hm9VRfuE7Qta1uaCqmttOKAKBAD9qPMIubRhN0zgpY3zbt50HzCsmnMNs42zT6fRec6a6OhOJBO9617v41a9+RSgUYuvWrdxzzz1l3/P000+zYsUKHnroobxR7nTinAC+UjEb4JuNeHWuTDi1E0S5KJYXKy75nY6sshBkhWhDSicPQq4LmQzzCnypVIqenp4zUkItF/MxgRdnLcVNEZONoMb+YuEwgklUpEFCTJjUuwGaCXOnvZI0FtdZi/gn/1tUqwCtVLADiVQ5MeggPnQELVSwWNawIdTOs1onrqvQlMBxoDohGDaHCbmV9GdyC40PB9ag+ww0KWclJn26s5XVqp5/jt9Gb28vS5YsKfmaYdI8arxJRKZodEP8kXUBdQS5xZm641QIwVDA5AnfUXLaAzpb6OXC2iVszNbSb2RwbAfLtHBdl4PKpm+4b87Sdl6cq8D3r//6r6RSKe69914+85nPsGnTJnw+H1dccQXXXXddyfd499rf//3fc+zYMZLJJD09PVRXV4/rZp0szlngm8kenydllclkaGxsnJEb+Fz2EqcrLzafwFeKi2cYgq1bc/spSkF7O9TP05ZHOp0mm80SiURobW0tmz3PNAqBz3VdRkdH891tpa7jfI17k93Ok8Z+bBQ6ghvt9nF/r3H9rDXr6A5mkULgVzrXOYuIigyH5Qg75QBKKHSlMUyGkKtTq3KLDh1Jixvm732/Y582hK4kC1QlIXw0OWEcDTJYKCX5g/gaLk820hxspo1qfqkfIYiBjmRRZTW1jp+7E0vxuWJcR59X7usKp/htZS+uLlhLI3c6K7DJ7cEVgrt3jc9EmW6qcuAPjd25LBY4KRP8WN/Lp+yN0z7+UTmKI9x8yVcqQW8gTVgPs9ioI6JZuUWBUiy2KgkGg+Ok7bz7q1BrcyppOy/ONPBNRxC7sNQ5n9HW1sYTTzwBMM6B/a677uKuu+6a9nG+/OUv85vf/IZ4PJ7v7HUchzfffLPs9sg5AXyzLXUW7qXNVtpsNnuJhby4qqqqKXlx8wF85bh4HR2K97xngHi8jkBAcMstLnN9Pj0x3mQyiaZptLS05MnM04l0Ove/poay5+IBnkd09zQQPWmywtX6fE3ca1UD/9W8hJMizkJVOYH7JYXknvgyekIQF1kucluoxIelHFbatRw1RnCpxlAaoFigKgFFhWswIJK8rJ2gTyaQCpLC4oiIslhVEVQ6I04Wv9C52G3mpVAP9ZaPZpq5yVkyjrhu4+bKsUHw+leGHv5HQldeg1y9hv7nfsVPlnSjOjpwUjYnxSBbtINgaPikzq1uBzeIpWdcymoq4IuK7Lifh2V6wmtsXB41dtElRqknyMcK9urWOHUEDB0TZ8xZAlY7uVXeR631PCx20C3iNKggf+xeRHX1+FJpIXk8Ho8TiUQmmNYGAgF8Pt+ETGomThDzEVNlc55U2tlIYIdcJexXv/oVL7zwwozfe04AH5R2aDBNs+RrPddzz6qnkPg+05gJKM1WXmwuItLT4eLlyo82a9bM3TKlOKNcsGDBpB1ek8XWrfDrX2tYFjQ3Kz7yEZdSHH1vAhocHGTBggUEAoG8G4THWyrW3nRdl4GBgXET1Gwmo2r8VKtT11GheE2eZERkadF0FlpBLlJN+brni/I4L2ndmMJlSKRY5FbRIxMIclnH7fZyDmhRKoSfAZkiIWz8Sicp7PxrwsJHBocaAthj3LN9/mGuLnF+3h6kQrFfDDHSeYDKv30QX7iSuj/+NMf/z7eI/dmVtK5aiaispJMoMZVmabYS0zR5wn2T6p40jXplfmWdTqfHkZ/LxX4xRL9M0uQGOSCj+NC42VmS238rE1MB3wJVQVRk8k4UbWriYupRYxdbtC4kgmOMkPRZ/JWZu0r1hPhUdiNP64ewsFkeqWV9a84BPIyPPzcvm3TsyWyJHMcZR1GJx+Nks1l0XR8HiLZtn1XNLd58ebYCn2marF+/nkgkMq7U/HseX5koVYIs3NeayqpnLuMUx1zlxWaT8c2Uizeb/bLCcF2XoaEhIpHIhIxyZu4S8MwzEl0HXYdYTPDv/y64++5T55bNZunv7yeVSqFpGh0dHQgh8iIGhRNUofamaZp0dXWh6/o4Qnnxan26k7sXFg4/1PdwREbRkaQq0tzhLKGJJgBeld183fc6AqhXQdpUJSMiwzK3hoDS+IC9Fg3JLjGIhmSxqmJYZNCRhJROIyHqVIC4MEkJG1OkiKgUQdnM2jKu5wrFw8YOnpOduKsUy59/iPuv/0siX36IakOy4IbbEMHciiIrXOplOH+P+HCo8jXTkAmRTueyqoGBgZwbud/ltw0D2AaspZFbRAeGfqpi8TP9AJu1LjLCYq8YYp3bQBgf+7QInzEvLUsuLwa+ETI8o3fiKpfrnMV8yDqff9H3MiBSNKsw99lrJhzjuIjlxxAITsjxNIn1qon1VhO2bdOV6ir31U4rNE0raVpbLHyeTqfzDTbFMm2nowQ6XRPasxX4vN6Jz372s1x99dUEg0E0TaOuro53vOMdZd97zgBfqYzP+2ILm0dmojE5nShX6pwvebGZaF0W++K1tbVNazN4thJvnpJNf3//pCLSMwFVywLLEhSW7z0DBtu2GRgYYHR0lIaGBurr6+nt7R1ngVQuvL2MQhsjz2ancIIq1D0sLJeW2gt5Uw7kJ/qQMtjg5iSzdgUiXM1KPsYUpAAAIABJREFUhkixVesd2zcTREWGJaqaq5w2ljo1uMKlUvlACBzlognJpe6C3N6TSNCoQixzaxiUabI4NBJkmAwZHDr1ONemWvPi0MWxQ/TzqLGTEWECikPLLCo+fCH3fucNDMvl/tFlbHEDWMKhw6llrxbBw6QaFaBd1uILafj9fqLRKO3t7bjK5Uvay0RVztvwqDNMIjLCxanG3ATuN3im+Qi60OjXUthCcULEWasaOCpG6BYxFpcQ0PaiEPhSWHzNt424MBEIduj9/Hn2MjbZ55f9nhtViKNE8w08jW7pZ+500gtKCZ8PDg4ihCAUCuUFvL1918L7bTYuDKViOs4McPYCXygUYv369ei6zvDwMJFIhHg8TlNT0++Bb7LQNA3btunt7Z2V6/l0YzIB6ULS+1zlxaabMZXzxZvOGDPJ+ApFpDVNKwvqMwG+YBDa2xU9PQIhchngunUOg4O5bLLQzNfLROYShZJZhZ+tsHQ1PDxcct/Q5/fx9cptHJJRuuQoAaWTFBZX0JS3LBoR2VzDiqqgTyTQEGSUQ1Y5PKd38qY2SBqLC91mmpwgL+snsYVDFofl1OKg6JSjXOA0kZRZEtLCwaUSHwlp8WKolyWT+P+9qJ1gSGRwUAjHwUmO8OotK6g+fyPOq69x7Yc/xB/94iX0hhwBf7c7yBtaHzqSW+1lebqDUooeX4p/9D/DoEjRIxJc5i7A5/MRArLBMIszi3NNINk0pmWSzbhk9AyW38R0JaaTxdHB72qUaygtBKM35QAxYeazNxOXbVovtznlOVwfs9aTFBbHxSgNKsQnrYtKvu4/osvS7/dPSrOYjgvDTPaqHccp2wBytmZ8o6Oj7Ny5k8bGRu644w58Ph+BQADDMKZ9ruck8Nm2TTQaJZPJEAqF5mQCO1UUg5KXbbmuO2/yYoU8uFLHmo3GZakxpluOnCnAzrSMummTy5YtgkQCFi4cxefrJZ0OTsgmTxedoZTuYWFjQyaToTMxwI/UfrZUdGKhcF1FVGSwcXnRZ3NeYi0WDotUFQE01rkN1OInKSzuM1fztHGEzVonMWlioPGy6CahmSxXtTSqENu0XhylOCpHcvQHkeVCp4ntqp8GEUJHoit4KdjLH0zyOZLCwlQOtnRBB1EVJHvJBYxUnEd2Qwe/+mCCixpOte+epxo5zy6hQqMU32zdR79mo1CcFHEC6FzkNhPH5AXjBMdljA2BFm5zOrhJX8HL2klWqhAZMUiTHSLtWGwYqSM22EtK1yc0HnkL0sJ7vFr5cXGRYwCcA/ypn+MwPj5vXjnl6840oXyy8Sa736bj0VeuGjEdE1ohxFlnQtvb28sTTzxBZWUlmUwmf910XSeTyXDJJZfwoQ99qOwxzhngE0KM61ysqKjIa2qezvCAr5AD2NzcTHV19by27Zdyep+pL95Un2MqUCjcW2tqapo27WOmAKXrcMUVcfr6ctJpra2LSmaTMz3uXL6Pwn3DWIXiO74jvCmiWLrCxMFQGg4uWcckqTR+Ej5EJJVhU3YdtweXsCM4RLus4jy3kWbCfFnbSkY6IGCYFDEkGpIIaSxcBIIuOYqNCwgs4ZCSFi0qjKNyNIo625fnBpYKF5cmFWJIpVGA5guyQlsCCvyr15DCJmqlaVTlS+E2Lv2+NGAgENSrEBlshBIcllHqCLBPG2K3FiGsDN5vr2WlU8eQTPOAeyUpLMK6j7aaSlT15Nm0N/F7GdAqo47LnAW8ovUAsM5t4CpncmHtmcZ/RJfldJ9Pb6Iv5dFXSuu1EDyna0k0HbrDf0S0tbXxx3/8x8ApR4t0Ok0mk2FkZIS2trYpjnAOAV80GuXkyZNUVlaybNkyDMNg3759p31c08zJYB0/fnxGYDDTKAS+2frilYtyIFK4R9rQ0DDjLtiZAFQmk6Gvry+/gCjH+5ttQ85cVvq9JPj/fFs5pEUZJUtQ6ThC4QiFjqRaD1JFkBGfxYDIcjg9xIKBAB3ZHF9K8ydIBGyqGg0CPklG5KDNh4ahNAQCS7gsVJUcYhglcgT5GgKYONzktnOIKK6AlMpwRaaVEh6yADSpCq5y2zjmjuLiEsSgWZ16sR+dCjW1VJymBC1WiL6gNfY+jXfbq7jXXsOn/P+e30vTEOzVIlznLuZi1cpO1c+zWidB9LzD+1TZtLfndfLkSWzb5hp/FRuDlRh+H61GNfgVyPl5vpRSZx2hfKootsWCiTSLRCLB0NAQlmWVtMfy5orTRV6fzJJo8+bNfOtb30LXde655x7e9773lXx/RUUF55+f28cdGhpiZGSElpYWwuFwnjc5VZwzwFfcLVlIbD4dN/d45RPBsmXLZqU3Od3wuhaj0Wieizefe5al9hELRaQL99Zmc+5TAZRlWfT39+c3r2tra/PfWyYDmgZzrVbPdUFyUsT4Z/0tMuS6R6vwM0wGF4WjchY3CWFiS4UU8JYRZZFRx6XOCoRi3Er9/JFKMr4U6ZBJ0lD4HMkldiOd/iR+pXGns5JOEWWvHGJIZrCUw2qnnj+xNtAjEuyREcJxlwuy9ZMC323OMk7IGIbQ0JTgbnsFR+Uoh8boBXfYHQSnUTpUSvGpvrX8NNxLVGRY49bzSesibFyq8JMl1yThoKhXub3SnaKf7xlvIsdoB8fkKA+Yl5dUjCnMpr1SXlNTU54mkMlkyKayDGYHJ2080jSt7PebxuJ7xpscFVGWqGo+aW08K7UzZxOT0Sw6Ozupra3Nc1s9moUQgu9+97vU19fT3t5OTU0NbW1tp9WSyLIs/vZv/5Ynn3ySYDDI+9//fm644QYaGyeW1j1u4euvv85jjz3GoUOH8plff38/n/3sZ9m0aVPZ8zingK/Qk6+wPDifN1upDtHDhw/P2/FLhcdN6+zspKKiYtaizuWiEJyUUnm+YTgcnvN45YCvnEOD68Ljj0uOHBFIqbjmGpdrrpnecctFuQnPxCGNTQXGOPWSXaKffzH2cVhGGRYZRsm5JCQxEYBPaCgUKRwS0qLWyYlCR2WGHaqfjW7LuEznk1zFHt9vWCYMVliKlDJJ2Flujjby9qGF1PkqEP56XqipYNSwsXXBKlWPjWKRquIN+jjgj3BUH+V+qgmUALA2VcVfmFdwXMRoVmFqCHC1m9srK+WQUC4W2xV8I3vzuN8ZaHzMXM8Pjb2khMX5TiP32KsA2Kn1j6MUdIlRRshSS3kaT+F3MxlNoHAB4e3llyr1FXI0v268zpPGfjIiJ/J9RIzw1+5l/+kyvpmEUipvQFv4O9M0ufHGG9m+fTubN2/mscceI5FIcNFFF/HII4/MCQAnsyQ6cuQIixcvpro619G7ceNGXn/9dW699dZJj/XUU0/R1tbGP/zDP4wTHP+9ZNkU4am3zEdWVCgvVpz9nC5rokIunuu6tLa2njb1dI+P6I3n8/nmzVW+VONMIbhOBuavvQadnR61QfD88xrr1jl4bITZAF+5h/qgGOYZ/RgmDlUqwLvtlfmJ+gX9RG4fGZdOOUJG2bnGCyHwoZHBRpHLeqSAhLToEQl+I45yqTNRXNeHxnLqMJTE43QvlBW8X1uHU+nkm2iuGG7g+xX7SCuLk1qE17SjNFNBXyCDLSxGDZOf6gf4oF3a2DOIwSo1Xn9uMoeEyaLcQuEKt40rsm3jRKMBQhj532Ww6RMJfqt1cbOzdIIr/HTHgtKl0v0M8bI4jrQUb4+1YY2V+mzbzr/2twuPkVV2DowFvKr3kFbWWdHccrqi1B6ed/1uueUW1q9fD8Dy5cvzPOO5nt9klkSJRGIcpzYcDpNIJEoewzuHRYsWsXBhrmNZ0zSqqqqmrf50zgDffDs0eDEdebH5FpEuxcXzuICnKxzHyVMT5tPdHSZmk/F4rnHFMIyy4JpICIq3IEZHmRPwlYsX9ONjItASSzi8qJ3gDie3N+WN0ieTCMAULi4OjGVOtsi9QipwyBnJZnEYESYH5BC3uONFlDUkjW6IEZlzOrdxWeDmJoZCA9tDMk1QryCowLEdbMdmlzNAVcrIK4EcsfoZSrXmS3/zTdmZ6hrvlP0cFSM0qxBXum0IBHfaKzgmRzgohjkgo6xwa9iu9XFQRvkz62L8k0xNMwWHg2KYv/NtxRYK5VccDSX4onl1zoi3QFHFcE/x1hQKVwmGk6NUuj5s256yVDofcSYzPqXUtHh83gLidFsSVVRUkEwm8z8nk8lxQFgqfD4fjz/+OMeOHaOjo4NgMIiUkg0bNkwJgOcM8JWKuQCSJy8WiUQmlfqaj3GKw7NEKqYKnA6HBjjVTJJOp6mqqmLhwoXzPgF4AFVIg2htbZ3gDF0ca9cqtm9XyLFmhspKxTQauqZ1LqXCHCsBZrAZIUNASJ6Xx3lNO0mXGMVRClM5pIVDhTJIilzZ0Mah+JAKCCuDWgJ5A9jdYpCtWg8GGu+2V3K5s4CfiQNUKh8Xua3jrI28CCgdB4UuJLqhIwzJIlUPQpBKJnNekbKSEyLGfvMorREfNa4fFdBYYNQQ9AdmJKRc7rqVihfkcX6pH0EXEgeXASfF3fYq/Oh8zrycp+Uhtuo9eQm1uMiyW0TYqFpKHm+mDSevaN35RYdA8Ibs5WvGNtpUFe9mZb5U+ll1JZ/XXiAtbAKu5KbUIsKOjmmZdHZ2TlkqnY8408A3FVXhTFoSdXR00NXVxcjICKFQiNdff52PfOQjZd9TXV3N+vXriUajPPvss6TTafr6+vjqV7/6e+DzYr4yPk9ebHBwMO96PpW82HyA0lRcvPkGvmIqhGEYBALzZz5bGI7jEI/HicViM+p8bWuD977XZedOgWHAddepcQ0uxSCWyWTypN3iB9404dVXJV1dFQQCYgKAJrHIYnNARBiWWRRw2I3i6MeIj5GobeWywqllRGQIYaApSQaLWgKksRgV5lhm6CJVTsqsQhlc7yxmv4jwLd8bKJHLOLZoXTSrEIbQiKg0DW5wXKkwjskP9ZwTQacYpUmFqMTPCreGu+yV/FQ/wHGy1Cg/K31N/O/WA7jC5URrDENp1NoG7dkKPty/ApW1cV03TwQu5UheLsplYbu0AXRxypvwLRmhXgY5JKOElUGTCo3bSXRwCZdpqJlpxhdAz5dUh8lwTI5SwwC7iXBADvEF82oEghvVEh7NVLBd66MSH++UyxkNRVFKUV9fX7Ir0hOfLuYczga8yvFwT0ecjZZEDzzwAB/5yEdQSnHPPffQ3Nxc8j0eteqyyy5j3bp1VFZWUltbOyPVq3MG+ErFTICvWF5s0aLS3LFSMRdrouly8eYL+BzHYXBwkGg0Oo4K0dfXN+8ZpUe7iEajGIZBR0fHjCeNjo6ce0Sp8IDPc4JIJBJomjZBf9MwAvzkJxUkEpLh4RA//anBffc5eM9dEouf6Puows+gSJEUDhc5zQyQZIeMsEBVYOMSFyYbVAu24zAoMuQsTwV1KkBA6Tyrd2LjErJ1NKBK89Ph1iIRvK71jXnA5TKTfTJClbsg10IjBC9oJ1jvnpoIfq4fpE8m6BYxLJFTevlT63wsHCxcNtnnMzgyiKZpfLNyL0ooTFy6ZYKwMliktxDRbXa1p7nLXjWODJ1IJTkW68PIKoJGgF3VIyif5BoWU+evnNFkaKjx32eviPNr/Rj6WDdnvwrRrqo5LEYAuMhtYpWqG/ceB5dfakcYFRnaDYMLROlssFTcba9kr4xwWEaJkGShqswrzhyW0XENNWtVA2vtU67u3sQ/WVdksZxdsbxYMYG8HKh5oHe2Ad+ZtCS68cYbufHGG6d8v2mafOtb32Lnzp10d3eTSqVoaWnh/vvv584775zWOZ/TwDcdsCjeT1uwYAHhcHhGN+hsrIlmysWbK/AVOkOU2qucz/2y4rFaWlpIJBLzXubxFhtHjhyhrq6O5cuX5ztg823w2SyHDyc5dMjC7xdkszmXgWefFWSzPpJJjdZbIrjn5QCpnjB+ZaEjcYTLsMiQxiYlLCqVj4PaMNfY7fiQZIVDvRvkuIwhhWCd24iLQrgOtuuyiiaWqhqe0zsZJsMIGWrGJmGJwF+gSNIpRnlNnuRitxUdSVyY7JFDDMgkEsEJYnzZ9yrVyo9A8A57GYuERiV++kjSJ3I0iFN2t7nPkxqjXnj7hlZI4+/9O+gUI4SUQdjWwFW4jsML5gk+3LmUahkcN6mXu+9udTr4vnyLhMjiQ2eRW0lGOPnx+0SSL5hX5UW3G1VoQjfp143X2a0NIhFkazJ8Iq1zDTWTjrlN9rBZ60JHcpe9ki+YV9MrEvxf/RC/03rzr/OhESjjBjFVBjYdOTtPXgwoWyo9V01oZxP/9E//xO7du/n4xz/Oxo0byWazbN++nQcffJDKykpuueWWKY9xzgDfbEqdk+2nzTRmAkrFvnjT5cbNRFKsMDwR6YGBAQKBwKTOELM9fvFYXldo4VixWGxem1AK3S6AfEeoZztUTPL1+yEY1NmyRWNgwMXng+pqi2DQRkqH1OYqLnvwOIvW2Zg7WjiiD2PpfswLLFplBYMyiSI3kXe4tcSlyQestSxQFfxCO4KGYFilqXf9DIks3TLJSquGDq2WHhHngBhiBfVERJqYMllEFXdbq4jKDI5w2S76aaeK7+q7+J54k49a6wm4kt3GIAhFQGkYQqNbxGl0QkRI8aD/RVbXVjOkZ7GEoFvEkEJioNEyRlLXlJjQUfoDYzcnRAwdjSGZZqc/xo3OkpzNT1DRHTZYk1owbmLPZDK4rkt3d/c4/pxhGLRTzQPm5fSLJI0qxK+1o+zQ+vLgFsbAjz7mOTgxUljsloN56oOLYpu/n2tYXvL1+8UQ3za2447t6x2Ww3w5ewNtqooPW+fTIxJ0yhEMNG6wl5DFmZSrOBvllnLyYt5Cq7hUWthwdKb87/4zA98vfvELnnjiifzzGwgEuOGGG+jo6OBzn/scN99889SC9GfiRM+WKOXQ4K3GCiOdTtPf3082m50XebHplDqLffFmyo2bacbnZbJ9fX1IKad0aZhLuRZyi4i+vj6ACWPNZzbpdYRqmkZ7eztHjx7NTyqTfYc1NdDTI4lENNJpl1jMoK/P4LzzXGpqXPyuy7FfLmCkd4CTL9TRUFdFoBqGnBitGx0s3SYuLVrcMHUEMccI2z/TD9At42hIemQCF8VGt4VGy8eATOGiOCqiLKASDcGlbitJZXK5s5AgBoutSl7WTrJasxgSGTrlCALBN3y/Q4xlhFnlYKKoVEYeHA7JKM6YH1+XEadBhLjIaWZEZFnvNLJS1WPicJmzgOVFZcUU1riMy0blmmfynDs5YWKPx+OMjIxQU1Mzwd/Qm9hrAgGk3+E23zIGRJJuLU5Q6dxjrSrLFzSQGEKO2cLm8tVynn1vagN50ANICIu3tEGucRZRgY+/Ma/hhBjlfxk7+Xf9KJu1Tu50VnDXGL+wMOZLucWTF6uoqJi0VJpKpXBdN3+/zrRUOtOYSo7M6/o824AvlUrhOA6BQADTNPNVqZx0YSuJRGJa1+mcAr7iKJ7MCx3XvQaS+bjxy5neFmZc0/HFmyxmAkxe95NlWbS0tEwrk51txpfNZunr6yOTyUy6iJgP4CuUMiv8TOWOHY/D4KCgpUVRVwcNDYpYTGBZkE4L9u3TaG+H1lbJNdULYaSemrBOOG2QjDi86SQZrR8lvFCS9LmMuili6RiLRS2tboDBcCo/qWdxsETu+jW5ISpsnetZjKk7RGUGhWKfiHBAi7JfDtOmKlmuatlotzIoU+wTQ0gkagyIToo4F7nNOZAbawhZ5ubKf6ZwqXcDubFFjjtYR5A6FaRd1fC+Eh51XlzitvLGWEZWoQw6VA2CXKZV7wa40Vlc8n2apk2Y2L19w1Q2TTKVJBqNYlomqcYYJ6qG8UuDw3aEFaJ20gnWQOMuayU/0fdhCUWN7ecus2NSq6VmFcZBoRWA6WL3VDapIXlF62FApnKUCQE/1w5zo70k78LuxeluNikslfp8PhzHoa2tbVInhuImmrl0lU5HoBrOPmeGbDZLMBiku7t7giant4ifTpxTwFcq4/NEXb0GktloTU4V5ayJ+vv7p5VxzWaM4pitiLR3/JmAU6E33lQC2XMBPsuyeeaZKJFImquvrmL58hqEKD1O4RgHDgiee04DclzARYsU//ZvkEjoOE7O8iibhUhEUlfnEAwI0pEgYVeSzcKuXQb67nWYSpKtT7HyohQtK5PckllCRzLMAbOLNxuPk/YplrhVhPyCuJ7LWBSKdquSC4wmep04f6f9jhGRISoyNKggWeHwlhjEdVyupo1aFcg7iocwWKgqGRJpGlWIWidACpt7rJUsVTXslRFcBbZ0yZKmwQ5QoftzmZLSuMFZzAgZDsko7W4VTUUIcoPTjqYEO7UBqpSP++y17BGDZKTDJU5rvjFksutaGFKT/KD6ANu1PnQludNeic/184Y+gnIFR+Qw24wX+M3wAT48vIoWo2qc4a/XWHKb08HlzgKGRQbZH6O+dvKmsmudRRwRI7ysn0BXknc5y2lX4/cDM9jjskxLOKSFRZUaD3xnUqS6sLllKieGUqXSUlqb5eI/qwltZWUld999Nw8++CAf//jHqaurIxwOMzAwwKOPPsqdd945reOcU8BXHJ6qt9f8MJ8GtIVRDErztXdYbozCKASh+vr6WQH7dDM+13WJRCIMDQ1NW79zNsDnui6DgxG+9z2X/v5qKiub6eqCdesUR4/mgOvSS10aGkofe9s2jVhM8OqrOpkMtLW5hMMuIAGB3w9KQVOTSyikOH5ckjN8VgwOSnRdURMSDPxqJcIVZPeOUHNdimuubydeZfKYr4sGVcfv5En2qxirM1VcGq3FcTM0KB83xVsYrI7wdP1BFqgwOhIlFKNYKJEFFHu1IQ45UW61O3hTDLBfG6ZOBfCh8afZi3lLGyQjbC51WzkhYnzLtx0XxTX2IlbbdZzMDvGOzBKaK+qIygzrnSYGRIpv+3dg4iAUvNdezaXueIeSa93FXOueyuw2qlaYophQ6v59RjvGVq0HDYkpXB7X93GFswBDM+jWY0SliUJwstHhqZp+/lt0waQNIYFAgMX+MH1uvLxyC4I/si/gw/b5iLGfi+MKZyGvaT04IrcIWe7WlXSgOJMi1eWAqJwTQ2FXqae1OZ1S6X9WE1pd17nrrrs4dOgQX/nKV2htbWVkZISenh5uv/32PB3i93t8JcKTFxseHkYIcVoMaAvDK0POhy9euTGKgakQhOYqWj1VxlfYUDLTPcqZAF9hg0wmEyYSaaOqKvdw9vXBkSOCMaUlXntNY8MGH8uXnzp2Ngu7dws2bxZs22bgXbKDB3XCYRddVyiVAz2/P+f23tgIJ04IgkHFkiVw3XUWr72ms+vNEKbIklIODbqf9sNNcL3iDdlLFhtTumhSpx6N2mAlKhDiU5mLMIfixGWS7wV2s4M+hKly4GBYDGsZJAKf0lirKumRcR7WtxMQBuepRpLK5P3mGi5Qzdzu5lRjtsguHvS/iPft/0Y/ylKrmnfHc8Lotao2D1w/1Pfg4OZKgQJ+ox/jUnPm1lwnRZzntE5cFOtkFYvExIaoiEiPkz9zhEuDG0IoRUrm9hJ1JajGx6CRIlRVQRVV+e+5kDsXj8eJRCJYlsXAwADBYDBf+ivFN5Rl9g3XqAY+Y17CVq2HIAZ32StKvv5s49UVx2RdpZZl5Rtpik1rvWvmiX2XOx84+4APcnJmX/ziFzl58iQ7d+7E7/ezYcMG6sYkm36/x1cUrusyMDCQlxdrb2+nu7v7tIKeN24mk+HYsWNz9sWbLAqBrxiEyqnKTDcmy/gKm2Smclovd+zpAF9xg0w2Gx73YKbTOa8+L3QdolE9f+zRUfjWtww2b9Y5eFAyMjLWsCFASjBNCdj4fAq/H9rbXWpqFPv3SxxHkEwKdu92qahQmGZOc7PKDrCi2WW14ScgFWATQM8JUJMZ2x8DzdU4+GwN//uky7rqCsSVfVjVBvWygrSw6SWOpXJZmFRQZUls06TLjWAZoGs6mqZRLfwc0Ua4wD7F6euUozhCIcaIgGlhMyhSQN2EScDBLfp55iXmOCaPGrvy790b6ufezFKK2XXrnSY2a8fxTqFWBXiHu4xGK8Q3xRt0iVHaVTU+dOrcQF69JfedTO4oUFNTk6/WFJqvFu+BlZu016gG1hRw9krFmaQYzNdYQgh8Pt+ERWfhIiKZTJJMJnFdd4IlkXfdvPOZL+B3XZcvfvGLHDhwAJ/Px0MPPUR7e3v+7w899BDbt2/PZ7Tf/va3y0qWpdNpent7SafTKKXYtm0bUkqWLFnCypUrpzyfcwr4BgcHyWazeSBwHOe0iEd7UUjQBk5bKRVOGe162dBUOpczjVIZ32yaZCY793JlVG9vMp1Oj2uQCYVgwwbFm2/m9ulaWhS55yZ3Dkop2ttzTUWHD8OXvqSzfTt0d0tMU1Bg1oEQgro6F8sCw1B0dChuvtmhu1vwyisaw8OQSgmqqgQ9PRKfD+6/P8v27Tr79mm8+qrG2rUuW7dKLrmslT1uhB4tgeWZvT65hJOv11BLBf0pl4p4Db5NMS50m9kjB+mTgrDyUS2D9IskKamI6w4finXwc45gmia9MsGoZiHTJouSBquMRvx+P81aiIAyyI5x8nQkK916SsXlzkKeEgdy1xyXS0oIZE8VB8Uw1ph8W+7awRF/jI1Fr1unGvm4tZ5XtJPoSN5trySAzlVuG5dmWnnE2MVROUK162dTkYh2FhsfWslSZTAYHLeQKzRfzWQyMyr5lYszmfGd7rGKS6U9PT2Ew2F8Pt+EUunXvvY1pJS0t7cTjUZZs2YNLS0tczq/Z599FtM0efzxx9m5cydf/vKXefjhh/N/37NnD9/97nfzWdtk4QHyK6+8wte//nWam5upqKggm80SiUS49dZbfw98xdHS0jIO6Lwsab5vukIrnerqapYuXUpXV9dpLRt4N+/g4OC0dC5nGoXg5DUDJRIJGhscsy8XAAAgAElEQVQbqaubmFnM9NinRKpz5chAIHcdBwYGGBkZKdl0JATce6/L+edDPC644ALFwABs25bb4/P54ODBIKkUfP7zOv39gr4+sG3GaWdqGkip0DSoqbGpr9d4+9tdrrvO5ZvfNEgkIJnMdXvG44IdOwTr1ytaWwVXXung8+UcIqSELVt0GhtdxPMbWOZfTutlxwisifHaoUo6RBUhZZDVsrgH66lQ/SCyXOQ246CwcDhEFB2BiUNUmrQEa/mAuIB/DrxFWgg6nHpa/dX8m+yifkBHZW2Wu4qbm5t5qaofqUluMzvIGhY/qjrBAlHFXVRgowhjcIW7kFrLzxE5wgK3kotUaVmoctFCeMy6aIxgrxS1TmnZvkvdBRP2ECHXrfkn1oYJvx8lyzd9r3NSJKhSfjZZ61irTnmylXpWC3mZnq3NZCU/GE8kDwQCk+qUnsnmlv8IAruu6yVLpQ888ABbt27l0KFD/PjHP84bdj/zzDOzXki/8cYbXDPmGXbhhReye/fucefS1dXFX/3VXxGJRHjPe97De97znpLH8eaJAwcOcPvtt/Pxj398VudzTgFfqTb6QufyuUaxcLW3z3U6M0tv3zCdTiOlpKOj47Q8rN516uvry8uZrVixYl6um5dNdnfn/PVGR8HvT3L99SdYsiRcdm9SCFi3Djx/hCVLYPFixcMP53z6Dh5sYP/+AErlskLLKrw2ufdoGvj9itZWxaWXxli0KMTHPiZ44w1JVZXCsnJanl6WuGuXRjbrctNNp363c6cknRbouiIeNwgGBVCLPFjLbXdbVPp1Bgrku8I+wSes9fxO9iGAt7OUr/q2AgJLuWgiR1142NjOx6yLuM9ay35tKJcB+SDts6ha2EA9IRzH4dPZhXx0KDfJP+U/xP8NdKEJyTOB4/yd7w0aCHGRauGL1jWsVg2sdsqX+crFQlXJzfZSfqsfR6FYbzZwgVk6w/RiiBSH5Qgdbg0NkznjAo8b++gVCTQESWHyf4y9/E/zulPf2DQXqYUlPweXn+sHGSHLxdlmmtMVE7oji8HQ5/Od8eaW0+muUmq8Up9NCEF7ezvhcJgrr7ySNWty1JdUKjWn6lEikRhXstY0Ddu20XWdVCrF/fffzx/+4R/iOA6bNm3ivPPOY/Xq1SXPD6CmpobDhw9z5MgRQqFQvgow3XM8p4EPTqm3zGUCL1Y/KRau9ib2+cwsvU3+WCxGY2MjLS0tHDt27LSAnuu6eR1C27ZLWi/NJbyM7+mnBaOjJolEgnRaY+/eZVx11cxv0UOHYMcOwWuvCfr6AqRSGq5b6rp4clGK1laXBx4wSaVSXH+9xDCC2DZUV0N1tUs6rY1NhOC6gqYmxebNGu96l83u3TqZjHcs2LtXY+PGXHas69DVKbn9dpsf/tAgGoXBQcnq1Ta6HcS/vR3ThPM3unxUrM/Jask+JAJ3jL6wU+tnsVvJK7IbBTQSZIlTnRdzLrQpAoj5OqmjluFkjMOBGFIJwpbOi+4xvjkC78+snnH7e3Hc4LZzvbkYBYykojhi8oXddtnHI8ZOssLGVfBxaz1XlnCaAEhgjitvJoQ5zstvps+QQvEl36ts13LX9Dd6J/9Dv5z1Facy3UKqQCaTYWRkBMuyUErln+m5XKvpxNkmWVacDMx03744im2HCnVAg8EgmzZtyoPW5Zdfzv79+8sCX1tbG08++ST79u3Lu8OPjo7ykY98hEsvvXTK8zmngK9UzMWTr5CLp2napFy8+cwsC0WkCx3JHceZdxFppRSxWIz+/n50Xc9/xvkOr4x68mSUdBoqKyvw+fyYpoKxZoxsNgdo4TD09uZ+XrcOmpqKzznX5LJrlyASEWSzOuUuixCKYDCXCb7tbS7d3RkMI/cdbtzo8uqrigULYHiYMSBUhEKKgQHByIiGrue6PXMdodDWpti1q6CT0YHGRsWSJYrPfMbk058OcPSozqFDtfz0pzpr1rgEAvDSS4qPfmIpPzwxysiKAYygy4pgJZX4kQre0iLUqSA9MsFL4iR7VYQuX4w/ty6bYCQbVgYjMoupubgC/FInIP1j+W2A4Giw5F5Y4QQ/LZm8MTiaCoz+VT9MSths03oZIcNL+gm+kbmZdzoTZcdWu/UclsNoY2T9ZW5tHvRMHA77RgnJFK1Mz3B0lCzbxxYSkOPs/VY/znrrFPAVLxwg95wdOXKEYDA4bv9L07QJTTTzoapytgPfXGPDhg1s2bKF2267jZ07d47bh+vs7OQzn/kMTz31FK7rsn37du6+++6yx4vFYnz605+murqaeDyObdvEYrFpz0/nPPDNVoprplw8b5zZ3kyFws6VlZUT6ALzvV/peeO5rsuCBQsIBAIcOnRozsctDs85AWDlSoPjxyuRMlc+XLo0N1XHYvCjH+UaUl5/HSoqYPVq2LVLcd99Li0tcPQo/PVfS44fz1kUDQ0J0ukcEOYuhyryxMtdI78/B2SgJgBkMAi3325z+LCkpsbFMEApbz9P0NKS+51lCc47z8sG4ZZbbLJZgWUJVq50qKpS/M3fGOzZI9m9O3esdFrwu9/pdHa61NbChRc6/Pc/D2KIK1h5XjPdlx1AW2kTbjW4xmnjX+R+lqhqThDDFi4xzWQvEf6neIXHsrePO+977FX8s76bqKtR6/qpkLn7REdylVxMdXX1uL0w0zQ5ag3xlLYf13K49ngDlYwv/fn9flK6Q0JatKjwjFzabVz2ygjDIgUIksri73zbuDXdMYFGcKuzDB3JERGlCh/vHVOZiZLmS77XONTaR1XwJHc7K7m7hMxYcfjQMBgveaZP49w9MnlNzSnyu7dvWJgZznTfcLI4G9zXi/8+E8nEqeLmm2/m5Zdf5r777kMpxZe+9CW+//3vs3jxYm666SZuv/123ve+92EYBnfeeScrVqwoeRzvOm3evJmPfexj+VLsTOOcAr7JSp0zyZRmy8WbjUMDTC7sXByF8lwzeYCiUejuhtranL9dNpulq2uAQ4dsOjrqWLWqOp+RzWdGWdgA5HVy3X9/iOeeU0QiitZWuP763GS1bZvAcQSZTK65JB6HZcsUPp/gzTcFjY2Kd79bY3Awp6ri90NVlaKiQpBIKBzHy7pzDSi5a+SilMAwFEq5LF+e4P9n783D7KrqdP/P2sMZq07Nc6pSSWWCTEwamRG5inppgRYa4kC3Ckq3Qyvd2orXxx607e6fSNstetXfvXqRRkFRsRlk0BBCQoIRCIHKVJkqqXk687D3Xuv+sWqfOlWpKQO5dKe/z5MnVXXO2XufffbZ7/oO7/sePHi4eL6llASDQZ59NsySJYpEQnHkiEAID6UEnqdIpWBwEJYvV6xY4ZFMCpqa9DSoYcDYGHz1qwG+/OUAgcCEFJoQBqkU40ArGByEp5+2KCuTrFmjaH61g/o9bSw6L83nP2IjUZQTwEW7QSgUAUztymAkyONOcixvVuX8lfMWDg0dRWYtfl59iKxwucJtO3bQRMDXotv4hb2XahWilRg7a1Ms8irIuQXas2W8baSBpwKHebSqG88StMkYf5l5E3XBGLZtz3nNXeS18Ctrr94ZigA2CZEnh0tkikC0QPB2bxGwaNLff27vZchIYyuBJQx+Ze3jHe7iY14/NSLYXOcu50GrE1dIWmQ5Nzlz3yynG2wp7Rv6o/azCVBP1zecCWxOtwntXP3LU53xGYbB3/zN30z6W0dHR/HnW2+9lVtvvXXe20skEjz00ENcffXVVFZWUl5eTiQSmdRHnC3OKOCbLuZb6pyvL95McSK2QX4ZVQgxL0kzH6Dme1w9PbBhg4FpQj7v0dg4Qnn5EBs2LCAYLKOzUzAwILnssok+3MmuTEs5hmVlZcXMdWhoCCEUb3/7sbwy/7T51TchJqYyLUvxve8JDhwwkFLgeeA4UFXl0dxcoLvbprxcUCiYSAmBgGTpUkVFBezbpwgEDJqa4O/+zqatrY2jR48We7b5fJ7e3lrGxkL090eJRg0CAUEqJUgkIBgUdHbCtdcW+MM/nHwNeR584xs2mzebpNMa6KqqFKapxhVgoLxcUigICgVNoQA4fFjQ3q4wCzbNlGOjn/w+ZyWPm/upVWGccW1OiaJaTd/MFwiCyiSqItzhrJvx87g9+DiPWF3khaSHJBnPIW9J0oZDo1XGS6Exmsqq2RJIUC7L8aTHkJflAeM13nNkQTFzsG0by7KKN/jSa+SdXgevOAN8O/AiAkEAgzWyYU7QKg1HeZN6f56SFPCO2UaCPClRoKEkK73ZPZvLvVYGybJCVU9aJMwU8x1smUmAemrf0O+R27Z9TFm5lDd3OmI+HL03okA1UDxH55xzDr/+9a/ZsmVLkco1NDTEL3/5S5qa5qbonFHAN90HPVep83h98WaK4wG+Un5cQ0MDsVhsXmBzvOD66qsCw1AkEkmSySTJZITW1mVEoxOXxbZtBhddJLGsE8soS2Oqc0LpBNZsJPZzzlHs2aOwLEGhoIjHBbt2wZvfrLO5r37VxDfZ0ER0hWXlueEGyRNPuCQSIcbGTCoqFGVlEscR1NZKLrpI4nmCs8/2ePRRG8exaWio5JprLGpry+jqgnjcZNcug5ERQTTqAR5DQzaRiMIwJIsXK9rbdRZYel7GxgS9vVr+zLIUhYLO+BYulCjloZRLQ0OQV1/VUmg+HWJkROuGtrUpbrrJKW5voargo+65XON28D+CmxgRWSSKK92Fs7oVzBYvGv08YnWRw8NDYmHSY6SIqQBBpa8BC4NDRoKC8jANA8vQfLCQWcbi6GI8z6O/vx8pJZlMpkgo9zUk/Rv8X8h1dMgqtlg9VMkgn3bmHkAojUu8BeNeenro52xVy5DIcJe9jbjIs0hW0qbK+am1mzweS1QVX8pfTBRdrmtW5TQzMyF6apzsAm+6vmEp33Bq39DzPBKJRDFTPNVuDKXx/9KE9lTFTTfdxC233AJQbPHk83nqpzb9Z4g37js7TTFTCbK0FDdfzcnZYj6g5Pe70un0CfHjjkdIWik13sdLEgjYNDTUI4TF1JcrNZFxnaiY9EzOCaUx27br6+F975P8678aLFkixjMyPY35zW+aRQUWv09n25IPfMBm1Sp44YUsuZweQMnloKVFsHatJBaTNDYqCgXJz39u0dOjbwR1dWVIKbjuOvjJTwIEApBMmmQyAs8zMQxdMq2s9EgmQakMmzbl6Ox0WLHC4/zztT+YZQWpqLCpqJAoZTA2phBCYVmwdKmL6zpkswFaWjykNKmqAhDU1Ei+9KU8TU3w5JMm2Syce64syq41E+Of8m/l/7O3Mipy9Bkpvm+9xIfdtewUQ2w3exgVBRpUhLVGlOgMNgZj5PhO4EVywsNFItGqMwKLRbKq6EruIVmkKhhWWfaKET3QojQRHvT3x7IsbNumSr+JSRqSpdnO+XaIi4Jn6VJ90MELGvNeRK5UdfxF9k08lthJR2gB7/Y6+HxgAyNGDoAXzX5+JnbRqmIEMTks4vzE7uRDzlpGyfJv9mukcThPNnKV1z7n/l4PDt9UH0iY6BsePnx4UqXhVLsxlMZ/ZC8+P374wx8WBb39EnI0GuXGG2/8L8my6WI6hwbHmVhZl3LxSktxJxuzlVSnZpXNzc2va1bpS4w1NlqMjLQQiQRxHFi1SlJTA/v3K0xTlw1XrFD4b98vpc732FzXpb+/v0i5qK6uPmGHhqoqKC+HPXu0HmcoBD/6kUF3ty4VlkZzs+ATn4AXX1S0tEi6ugokkzZVVYqGBo/uboOGBoOVKxV79hik0waGoW9AfX0mQ0MGu3eD5ym6uw1iMd0zzOUkqZTJ2rUe/f0GUhoMDJTT1FRGMql49lmHaDTJkiVJ8vlh1q0LkMnUsmdPiPJyg44OiERMHEeXa3M5QUODpL9fT5dGo7BsmWJkRBTBWAh44QXFrbc6LF06LhGWTpDLhWiotzBM6DFS/LP1O/aYw+w0hgBYLmt4sUrwufy6aecfD4o4XWKEMhVgTGh9UEsJvpq7lHVyAT+39pAVLmfJGq7y2rnCa+Pn1l7SFDjPa2StmlhZT/3cZtKQLAXD6dRV/Bv9TAvMJbKKPxhro6OmQ/c7x0EPtG1SmonvsUCQxUWi+PvAFnqMFALBy+YAtjK4vESEe7o4XRw+v28IUFtbW/xuua47Z9/Q/3e8x/mfAfiqqqqK19LIyAibN29m5cqV3HTTTfN6/RkHfFPDL3XOxcU7FfuZS0T6ZPlxcwGfn3nl83kaGxvp6Ihx9tmCI0cklZXgl8bXr5fs2QOxGEXBZ3/788n4pJQMDw8zNDR0XA4N0x17JqN7e4OD8MorsHmzQTisdTdBZ11TL+NYTJJISCIRyZ495dTXC1xXA9f+/R49PYL6+hxbthhUV+uMNpMB2zaJRsNUVSlaWlyU0vr+8Tjj2aLucTY3S1pbFYWCplXoBaYgHA7Q21vFZZfpklpbm0dDg8f99yuEyHPggEkm47JiRYGhIYt9+7Q0Wiaj5dBaWvQQTXW14sABg4nLT5Pply71+O1vDb7/q2oOeDaxljwXfPoAVtRlhzlARjj+sCq9RpKACNNlxannWBmodlWBUlo/EyCHwyKvguu8FYSx+YspfUEbkxvdY3lVfmwO9dJtHaScANe7y45xNRdCTFJXyeHwhHmAgpfhLckyZE4vOPP5PDsjo4xGPc5Staw0G8YzaGtS+dHCoFmW0WOkxo9PsERVFTl/JoKL3QWMkeOwkSiWgw0EO8yBeQHf6ZQrmwpGJ9I3LF08zAZac010+qXDNzLwfehDH5r0ezab5U//9E/n/fozDvimZhaGYZDP59m3b9+sXLyTjZlEpCORyCkRkZ66j9KYSnZva2srXvjl5TB1IrihQf+bGnNpapZOoIbD4eN6X1NBVUpNYdi9W5BKTUiZ5fOMK7t4CKGIxXTG5yfThqFB5F3vslm0SJc3x8bAsgzq6sC2w7S3GygVwHUdenpcbFsyOBhACMk556RYsyZFdbXFpZeWsWtXkHjcoLJS4bomS5ZIamoEQiguusjl6aetopuDlHDoEDzzjMmFF3oEAiadnUGiURMI0tiogTudDpLNShobCxw5IkildC8QHCzLIx7PY5oBJjRHIRDQ7/ORR2waTYt+O01mOMDehxu44KZBmimnmziq+CqBElAjpx9+qSTEH7or+NfA73BwsZXBqMjzA2sHt7tTVTdnj2dDPTwROUrEDKFQ9Bkp7ijMPFBTwOOrgS0MGBmwYVugny8WLqaOOn5u7uYJYxg8yWZvgGtGUqwcKEcpRSAQQEpJIpEgFArxKXUB9wZ2khAFlshqrneX8lNrD2lR4C1uC2tVPQU8ogQojNtTKBSVau4F7emesvSnsmeL+fQNh4eHyefzGIYxLTdzPlWbN7IzA+gF/LPPPktNTQ2RSISqqip6enoYGhqa9zbOOOArjXQ6zeDgIIVCgdbW1lPiizdTGIZRJFn6hPcTcTKYax+lwFTapywlu5/M9mfK+KY6J8xn8fDaa7psWVEBjY2TFyRbtgi6urQv3tGj2im9okISCrnk83q6Mhi0KBQUdXWaluF5mpfneYJMRtDbqzh6VNMdKiogn9fgEQhIMpk8g4MGyWSYSy+VtLYKDh8WhEKC73zHprk5S339KAsXChobQySTAaJRk44Owac+5Q9KGRw9Cv/7f5vk84J8XpPeH34YNm0y+Mxncti2Pt9Kwd69OsNcu9ahs1PQ1xfGMDSoKaVYutQjEnHo6oqzevUYzz1XiWFYtLRAJCK5++4wO3YIliyBNdE6ekWa9ozJnzltPJ3poV91UVmZZ8zOsUCWcVmyjiZr5s/hdvdcnrT287IxiBAQxOIn9i5ucdcc18TlvkC8yI0TCA6JBA7ejEM3240++ox0ceoyYRR4xuzmGm8JW80eAsIEwyRg2+xpzPMHNefgum6x5Fda+rs+2Fi8wRP0eF/w7Enf4QAmHyqs4V77FTLC4yxZw02zOND7cbozvhMF2dn6hjO5uPvPyefz0/YN3+jAF4/H+elPf1q0rkqlUsTjcdavXz/vbZxxwCeEmMTFq6qqIh6PE4vNTwniRMN3TkilUjQ2Np5yEWmYyMhOxhtvPtsvjZmcE+aKF1+ERx81sSwNWOXlMT78YU0yT6XgN78RbNkCQ0Ma+PJ5RXm5JJczxoFCkE4ramogk1HoCrFASk01qK9XxGKKwUFNZC8rUyxeLKmsTPHSSwb9/REcRw+VHD5skkwqamoEvb02Ugry+RA1NRX09EBjo0t/vyCfl/T05Fm0qJddu6rIZoN0doa58EJJd7fFc89ZbNqkS5ZLlsDzzwd4z3t0T7GnRwPjkiUelgX19S7Dw5qOAXqAJ5MJUVcX4OKLberqJP/9vzuMjmY4eNDl3/6tDKXSpFJhtm83edObCjRYIW5ea/PqCybP/2Ipoco6muuT/PVVZaxtCTGQ6UNUzG7aWq3CepBFaeVSieIls5+LvAkFjEfNLp6w9qMQvM1byB+4k8nFEc9CiYlFS1TZs5LEQ8pEldghKSA4DpJTifHmeP7qUyUsy6K5WXMRS0t/s02UXhhs5iLZgouc9wTs6RaoPtWC8lP5hjBhTTQ6OorruvT09BzTN8zlcsV2y6kCvrksiR544AF+/OMfY1kWt99+O29961tn3V5NTQ233norK1euJJFIoJQiGo3OamM0Nc444BsYGGBwcLDIxXMcp2gb9HqEDwzpdJpAIMDixYtfty+UEIJMJsPQ0BCWZZ1SWyKYnPHN5ZwwVzz3nODoUQ1yhgGuG+Xssw1++1uDzZsNEgk4cECDhefpjKlQMJBSZ3X++zUMDWqWJZBSD+VoeoBi0SI9tdnWJqmtzZHPp7n++jw33VTNXXdBY6PHs8+aHDkiyGY12X3hQq38IqW2MuruFmzdOrEqDgZDfOELMUxTWx4dPSo4dEjS1ycZHta0j74+TUswDJONGy2iUbj1Vpd//3fB8LBLNpsjGg1wzjkeixZJBgfh4EGD2lrFDTc442Vmg/r6IPX1QR54wObgQS29VlsryWYlra053vSmOOFwkv/zf5qR0iZSCBEbibI1ZeCcp0ilgrzlLbN/Du9yO9hm9jI6PuASIUaFnMgedothHrA6i+//IWs37V4Fa0qGW96VbGUs6jFg5ylTNjc5Z01rJ+THOaqBNV49L5kDCGCRrORKT98Ir3YX86DdiUL3Fd/pLi6+bmoWNlPp71T0wd4o7uunMnxromw2SzgcpqamBs/zKBQK5HI5crkcd999Nxs3bqSuro5zzz2Xs88+m1WrVvHmN7/5hO9bs1kSDQ4Ocu+99/Kzn/2MfD7P+vXrufjii6ddqPf397N9+3YOHDjA/v37+eQnP0mhUKCpqYkXXniBbdu28bnPfW5+5+KE3sl/4KioqKCysrJ40Z+oospcUdpXq62tpby8nGQy+bqBXjabJZVKoZRiwYIFr1tG6ZdPBwcHicViJ0TzePVV3evq6tJk8NpaRSoV4lOfslDKYHRUK3w4jiqKS/syZkLokqUu22j7op4enQEGg/r3WEyyZo3k7LNhxYosGzd6DAwILr64nLe8pQIh4PzzIZ3W27NtTSwPhQTDw3pfNTWSvXtNBga0YkwgoCXMslk9jVleDnv3hnAcCIX8PqQGX8cRdHUpVq0awfNMkkmLX/xCkUoV2LatnGi0nIoKg4oKxdatFvk8RCKa7lAogOtKhof1/vJ5g927DRxHWy0NDZm0t8Of/VmYYDDM/fcvYMcOE9uWLFmSI5PJ8fDDFjt2OEgZZedOxUc+kiIUCvGzn4Xo7DSIRBQ33eSycKHiam8x31K/x8BAoAgriy5zhJXjJq0HjDilGCYQHDLirPEmgC+ibD6dOg9bhAnO4KE36TpC8HHnfHa5wzhIVqraYqZ3qWxlSaGSQyLBCllDJZNLeHNd04ZhMBApkIo4LFf1WBhFWTZ/SnJoaGha3U0/o5zvvk5VnG6dzlI5MtM0J03gfu1rX2N4eJgXX3wRKSWdnZ1s3ryZ9vZ2GqZr/M8jZrMk2rFjB+eee24xQ21ra2PXrl2sWbPmmO1ks1k6OzvZuHEj/f39OI5DMpnE8zyGh4d529veNu9jOuOALxQK4ZY4kJZOdZ6KC30m/p//AZ3qKOX+hUIhwuHwcaX88w1fmqm3t3dW6bT5xPPPGyxbBp2dOpPr7QXXNRkbMwkGBZmMJocLYWKaFBVZSkMDoWR0FFxXZ34aFPUU6B/9UZ6Ojj7uvruMdLoKx7HYsAGamlze9z7J29/u8s1v2mQy0N6uOOssxbZtGgQrKhRdXQbDwxpMczmKlAl/P4WCwDQVQmiqQ0+PPgbQ/4fDBnV11WQyDlu2wOHDNoGAJtDH45BOe7S2FhgcDOJ5JmefDWDw058Gee45bdFkmorFi106OhSplGRwUGBZsG6dh5TwrW9Z7N9vEokIBgZs9u2ziMW000QsFiSdTtPZabJr1xA7dmR46qlKgkETwzD55jcFX/2qw1gwT7uMsYgYBgYBTMbIF8/zKlmLrQzc8VKmqQSr5GRLI/+7EzqO24lAcJaa3hqpSZXTpI69hudTErzH3s5j1n48FMtlNX+fv4KQmKBMlB7zVL++XC5X5Ib5U42O47yuZHL/fZ1ugerZsl3TNFmyZAmrV68+JfubzZIolUpNul9Fo1FSqdS022lvb+eOO+7gggsuYGxsjCuuuIKBgQEMw6CsrIy6urppXzddnHHANzV86Z6T5a1IKRkdHWVwcHBa/t+pzixLXRp87t/IyMjrAq7ZbJbe3l7y+TxVVVXzkgSaK8JhWLYMRkcVhw5BJmNQKIDjaENYIUwiEUUmIwgEFNmsBsBAQLsk6PKnzsa0eLTO4DxPP/6FLwiuuaaanp4YSomi3NnGjSbveY/kySctlixRjIxo3lwqpUHsggsU1dX6OOJxsG0Nfp6n+3HZrKY4OI4GXD1ZqsFWKYp6oC0tklwuz1O9tyIAACAASURBVIYNJgMDATIZg3RakMspXBccR7Jtm87sXNejvDxLYyMcPRoEFKGQhRAGO3faHDigvf6qqhQrV3rU1Um++MUgv/+9vlmuXu0Riej+6GWXeRw6ZAKabB8MBqivb8J1TSoqjKI3ZF+fS2fnEcLlLmWtkA5IhAGuCWd7E24PC1SM253zeNTswkFSoQLsMUZp9SqKfbwTETU4kZhrcXpQxHnM6sLExAT2G2P81NrN+92Vxzy3tA9Wun2/D+ZTK7q7u4u6raXZ4akik8/nfZ3qmI8zw6kE4tksiaY+lk6n51y4X3755ezatYv77ruPK664gsrKSnK53HEd8xkHfLMJVZ8I8JWO8AeDwRn7aiei1TldlLo0xGKxSdw/wzAmkfFPNhzHob+/n1QqRX19PZlM5qRpF/E4JJOS114zWLBAZ3vZrCKZNPC8cesYR1BerjjnHEVtraSmRq/0f/tbg337tOamYWig0tnXhLqMYegfRkbCbNwYwvMUkYguUWqbIsUvf6m5eZEIXHCB5OBBQWurJBIRjOtls2aN5MgRQSZjEo1qxZXaWkVvr96faVIE3d5eXYaNRj0qK7W02S239CCEyRNPNBEMaoD2PP0apQRjYwaFQojKSl3O7e83qKvLU11dIJFw2LfPolCwGB21WLasQKFg43m65PnAAwFyOQPX9SkUBitWaLL+H/1RgX/+5yDZrC9A4NLWZtDcrD0KbdvGtvXwzOrV7Ujp8plCJQ+p3aRlnhXxcipTGbqD3cUb/apQNcvsKu4MbWS3GGYrvTxrHuGvC5cUS5Sn68Y9237us3ay0xjCQFCnIjSqMgrCnfH5023bPz+ZTAbLsqiuri6C4Uxkcv88zSZCPVu80SyJTtafdGrMZkm0Zs0a7r77bvL5PIVCga6urkmPT3fcjz32GBs3buSpp56ipaWFnp4eNm3axF133TXvcuwZB3zTha+qcrzkcV8BBaClpWVWZfCTBT7fG6+vr2/GUuOpAtepzgm+03oulzuh1f3evdphIR7XNIVMRtMAfv97SXl5DiECSOl/EXV/z7f4aW5WVFZqAntrK/T0aFcEH8wcR/fVAISQBIMGPjYnk/q1hw8rensNXFfQ32/y5S8b1NcrcjlBWZnOMN/5TkkgINiyxSiWL//hHzy2btXyaBddJNm712DDBpNcTpFMwtGjAsPQYBmPC8rK4O1vjwMFrr46TGtrGb/5DXR3a4K8JioLQiENxqapeZQrV3r09wsuucRi3TqTv/qrCIWCgVISx4FEQrJ4cQrX9Xj55RCjo0FMU29LCAPTFLS2Km65RVJXZ/OZzxT4zW8yOE5iPOsN8Oqr0NenBbzXrfN4//tdTFNgmjbtdh2fYbxMFJ6Y/svlcqRSKYaGhni8vJuDtUOYpolpmOwyBtkierhELThtGcts+9liHmWLdZSgssgIh16RplZFeJvbfsL78sHBHwoppejMNVFaCojzUUl5IwHfqdbpnMuS6AMf+ADr169HKcWnP/3pGRfX/r3nscce47bbbqO9vR3P87j99tvZvXs3W7Zs4dprr53XMf0X8HH8nnylCijzHeE/GcNbnyOnlJoVYKcDPlcqepIFygIG1eHZgX0uGsTU7b/8MuzerW/8V16pmO67dPgw/Ou/GgwOCrq7dXYCGrwCARgZCZFKmUXfPF0uFGgDWsGhQwZr1nj09OjHolFV7PtJObnMppRBNqszskxG8+MqKtT4cRtEoxrskknt8eeXQMvLBX/3dzZf/rLL2rWS733PZGDAZMMGxVVXST77WZdwGP7X/9JUhcOHBZWVMG4jSDYLgYCHaeZpaJB88INlNDfrk3HHHQ53322TTOrnJZM6S6yr0yXPujpFSwssWSJ5//u1v9/y5YpDh/SEak0NxOMBysttlNLl1upq3dtUShIK5bntth4uuACECDE4qBWILrwwQn19A7Ztc9ddFn19Ghyl1Ka4ixZJPE9/lr5Rsh/T3egbRAHbGkJJieu5OAWX3r4jHHYljuOQTqcxTfOUlgCnxmw9vsMijoFBh6rkAGO4SK4utLNQVZzQvuYC87kmSvP5PIlEgkKhUKRizDRR+kYEvpNRkJoac1kS3Xjjjdx4441zbsf/PGpqakgmk3R1dbF8ufZkdByn6DE5nzjjgO9kPPl8a6JUKnWMAspccSJGsfl8nr6+PnK53LwAdirPLl3w+OWeEfKexJOKtQ1R3tQ8ff08lUrR29s7rXNC6fZ9oHn+efj5z01sW5fwenoUH/zgsefwgQcEL71k4Hmwf78gm9VeeGDiupqArv3yfKshLTTt3xeGhqBQkLS3G1RVSaQ06OsTdHer8ddMPh/BoJ6qzOd1z29kRPcINVFc0ydAFMW4XVeXBNNpgy99yaa2VrJvny67jo0JIhFYvtzgD/5Asn69RzSqwb6vT5+P3bsNPE87PjQ1BRgZCfD445I/+RMPIeDccxXf/naBsTH47GcDHD6sSCQMcjlBc7PHW98qyWRgdNTgr//aZtUqLUjtUzY8D4aHNS+xpkaD4c6dFqOjuq941VXwznfWkUqlJvV4s9nsePk9RG9vHbrnpy2ohoY0gPrXo/8PJsjLU8HwnaqDTcYRes0UElgtm3hPw/m4eae4COzt7S1mPaU3+hMtAU6N2b4753tN/NTaTac5RhIHA8Em6yjv81YXBbePJ04EjGbSKC2dKJ1OWcXXLD1dmfN8JMveqOR10JqmGzZs4JVXXmHJkiVs3boVpdQkMJ0rzjjgmy7mysamikj7pb/jieMxinVdl4GBAeLxOLW1tfP2/puakW3vTeEphWUILEPw8kCGtQ1RAubEtubjnFD6Hvzt79hh4C8KTRN27RLFrA101vX738PTTxscPKjthPJ5D9fV8mFS6gERw9C2PSWDtti2wrbzdHVpXc6f/ERnbDU1isFBiee5rF7t8dprYRIJUZQM06/Vx+O6jMuVGbiuIhyW4yCpn+hPayqlwa+sTB/Drl0GSoliqfXQIe2k7r/m+us9vv99k4EBSW+vQSjkUVFhkM+bNDVJbFuxa5fBrl2SdBp+9COLTEYfW02NorUVRkY8Mhm9rXe+U/K3f2uNcxlh+3aDK6/0AMnwsFadufNODz/J/9rXLBIJPXATiUBjIxw+nOSJJzzKypqIxcqxLLjkkhyGoblZ5eXJcfcJgWGYxGIOyaQkHA5jWRaOA5s36/d44YUepnksGAYx+Er2MjbaRwhgcLnXhmkaBCI2pmlSW1tLKBQqZj0+L2xsbGxaUvmJiCvP9t1Zoqq4sbCCL4Y2Uk6AOhUmYeR5yNrFh91zjms/c+3reMKfEp1uorT0PHmeRzKZPOYcHa+T+1wxnS7o1HijA9+BAwdYt24dlZWVvPTSS3R0dPCVr3yl6A4ynzjjgO94PPlKxZanDpKcSPjANJsLsy9afSJWSMfoXU7pxymlkMVMZwJc53JOKN2+f56m8kuDQcWePTqre+YZzat77TWKU4RSmliWQSwmiEQkAwNqvFypMAzNofM8TUaPRmFsLIhhKDIZRTIpqarKMzwsaW0t0NsbxjRtVqyQbN06+ZiTSV1KhYkssqoKWls9Fi+GXbugs9PEcURR/9OytAJMKEQRpBxHA18yCY8+KvjVryzyeW1vdOiQSzIpyWZthDAYGdEUCP90C6HLmt/7nkU6rQdf+voEiYTikkv01GhVlc4GBwcFhYIo9iVNUx/DRz86/UJMKbj0Ujk+2FJg164Uzz1XQVdXhN5ePQl71VWSF16I8vnPB6moqOBzn4N77zUZGZE0N+e55po0o6M5ent7cV3BD37QzMhICNM0efZZi899zsO2J74n/mLHkAbvcBcVryVXusdcM9NlPaUlwKeNg7wsBwiPCa5PLiYWiEzKDue6BmcDgVWqjjZVQcCfNi1RLj3eeD2VW6Yqq0gpCQQClJWVFcEwkUiQz+eLE6VTh2hO9Nh8QJ9ruOWN6MXnv+fDhw9zwQUXcM0117B+/foTAuk33rs7DTGdNVFppuQ7NZyI2PJs4WeWUy+q0v2djGj11IxvRW2Eg/E8piHwpKI1FiRgaLWE43FO8KM043v3uyXf+57B0JC2CFq2TPLoowYbNgj27zfIZiWFgi5rSmmNu5cr6uslLS2KigqPzk7B6Kigv3+CipDJ6AlIKRkfBNHlzP5+STgcIJ0OjWdpBVIpo0hHcV1NM9Afq+4RVlXpHt873iExDPj8512+9z2TsTFFfz9UVmqQ0aR3RWurdkvo7tbUg3gcKioU+/ebdHcLKio8bNshHrcoFAK4rkEyKcZl8HTJtq1NsWCB4sUXDbZtMygUBOk01NRoCsehQ3DllZJLLvFYskSRz2sXDN9I1/Mo2g9NF9XVitFRbfra2WkzNFTN6Kg5njHq8uzOnYq1a2HrVoMrrtBWU3/+5xpIe3oCPPpoE0LANde4bNkix1/v4jh5du/2ePDBUS65xCtqQB48GOH++0NkMoLlyyUf/aiHYegF3MjICI7jFL9D/sKu9Mbqg+Hmsn5+ah/CGDeTTRcO85ej5x1jUzRTP2wuMFqhaljnNfM7sxcDqFdRrpsirzbfON3KLaZpFidKS3v4c02UHm85+T+yJZH/2Z9zzjn84Ac/4Be/+EUxEcnlcvzwhz+ct/bxGQl8U8MHJKVUcVLzdIhIg3Yl7+/vxzCMk97f1O03lgV499IqDozmCVmC1oDD3r175w3mrlT8cs8wvckCsaDFyjKJU3BolIqyMsHVV+vMY/Vq+Jd/ETz6qHZTyOU0wVrTE4JYls6gWlsVtbXQ1CSprNQANzgIUhpEIhNE8UBAlyxdV/P3NFk9QDarB1psG/r7TbQ2JyVgp33tQJdLMxlFWZni6FHF+edrft3OF9JkewtEorWAotHsYeGKGvpGg+zbJ3Ac3UOMxSRVVYr6esHIiMTzFNmsxPOCpNMG8bjANCmWRGMx3XM8cABuv93jW9+yqKlRHDyoQTmVUixdqqitVVx3nUdb20TJ9bbbHB591KRQEJx3nmTNmumBT0rJVVcN0dtrcPBgFVIGaWxUDAzobFnzHzW5XkqFbU/ezuAgfPnLAVIpDbDf/a5FJKIYGhIsWxZgwQIN3k1NIaqq0uRyOYaHx/j61y2yWRfD0EM/0ajDtdfmGBoawrZtFi1aVHRO8AHQX1j6JVMhBL+3+zHGPycDwYFAkkBFpDiUMF0/LJfLFT37XNfFtu0i+XlqCAR/U7iUJ8z9pHG4yltEBSe2YH2jKLfMNVFaWk62bfuYRcPU7f5HBj4/PvGJT/DJT36ySIHI5/Nks9njunf+F/ChASOTyXDgwAE8z6OhoeF1cWooBabS3lpDQwOxWOyk9zcdsNZFAkSUHkAYYf7OCQCP7xth70gOQwheGUzwy0SO1VWC7aMDZJ9v4OXfm6RSgtZWj507tUxXNgv5/MSUpmnqUl84rAgEFB0dkmRS0N0tGB4WxON+tqQnLDXoaekuf3hFb2ei/Oj3BidAT4fm9YniZKhlKSKRAvv2ebS2Jvja1wI885SkP1GJMCWeBzErwmi+QMEOMzyss0YhNKFeCEkgUKBQkNh2kGDQJpnUHMNUamIwRoO0FpqeGKDRoKZv/NDcLFmwQPcRKyomA1JLC9x66+wTv/4CKRQKceedDfz4xxYvvKBwHNizRxPrTVPzDWMxyZIlkgsvnLyfJ54w2LFDn7dMxqdz6GPes0fQ0KCz1UsvNbCscsrLy8dL2jbRqEJKD9f12L8/Q09PH88+W8GmTTWYpslllxV43/vAtu1JoORfj1JKypSNRGnoU1rqLFhyC5qtH5bL5RgdHSWXy3Hw4MFjbHd8uTEDwdXe/IccZor/F7ZE843Z7In8RcN0GbR/Xmd7X34P8I0MfD7BfTb62FxxRgJfaamzVK6oqamJqqqq122lZ5pmUR09mUxSX19PdXX1Kdvf1KnOE3VO8GM462EIQdbx6E0WQClsw2B3l+SV7RmObqsgnYbf/MbA83Q/xdfWhAmPOmCcwK0d0wcGdKbU3S2wLA0ePqDZtuauBYNgWaKYMRQKejLUMCg6mPtKKXofE4R209SvbWxUnH9+gN/9zuCll4Ikk4r+rI1p5HA9iUeAlFdOwLCIj0ufgS+aDfm8Q22tixBhFiyAfF7S2WmQSul9+DJmGki0hNqiRbBqleLQIUFvr0FFBUg54WJ/zTUeM01dZ7Nw//0mQ0OC+nrFTTd5GIaWpPOHj8rKylAKFi+WPP+8HjB661s9du0SrFmjaG+XvPvdklWrJtNLlIInnzQZHdWDO/G4/j8YVKxYoTl+73qXyzveoShNpmIx7c04PGyMu4IXWLbMwLKWs2mTzvIcx+ORRxQVFX2sXJkulkj9f4FAANMyCQmLQ0acjHBY5lXzJ84ahNK9uNLMsPTGXNoPy2azBAIBKisrJw2HlMqNTc14TlRu7I2S8c03ZrIn8jOiXC5HOq2zeICjR4/O6NUHp96SKJfL8Zd/+ZcMDw8TjUb5h3/4B6qrJxskf+xjH2NsbKwoKP7973//lB5DaZyRwAeThzvKy8sxDOOYD+JUhq+C3tfXR3V19Ul7400X/nBL6RRqTU3NcTsn+FETsehNF/DGFwkRW5OqM55k136JZ2TIpUMU8r56x+QMDBgnl+sBjtFRj2xWMDIC6bRgZMS3nNEgohTjQyeiWB5taNBmsvG4BiNtyqoIhyGR0ECrpxAplkEjEb1N2xZs364HboaGNA/OdQQeASQ6u/OUiScVriuKx+qDaFmZyR//seLd73ZIp+H2221yOVEsyQqh92NZekAnFlNcd51LIKDLuq4rkVKXghctktx5p8tss1H33WeyZ48m0A8Owv/8nxne+c4jVFdX09LSgmEYvPii4J57LA4c0MLYCxYoGhrgoYcKzKYkp7mGWn1maEhLv2lOoH583TrJu9+tmHqvFwJuvz3F979fIJMxeNObQtx8c4inn9bkecsyxkuRIEQbHR0FMpkc2azu2w0ODuJ5Hs9Xj/Cz2v3UEUKZYcoIsE60HJMdlk6TTgXDUsPWqbY7/nXvZzzxeLx4k586HDKfScnTbUv0emSXpRm0b7uWSCSKNmz+oiE/3mDevHkzPT09NDU1EYvF6OjoOGXHdf/997Ns2TI+8YlP8Mgjj3DPPffwxS9+cdJzDh8+zCOPPHJazvsZCXzDw8P09fUVhzsKhQK9vb2vy76UUkWJMcMwqKurOy4x1ROJvXv3npIp1Ks7qnA8xdFkntZYkKYw7BtMsLcwgiOj5JWJY7iATmdmEnURQnPzEgnBrl0AgkxGA5WWGyudvpvIuiorYXRUsGCBJJn0rYf0c0IhWSSqlw7kep7e5uLFkvJyeO21CZuhZBJM4eAqig4CtfYQbjY2zqeauOlWVkra2tK0th5k/36bJ56oYc+eGsrKNE/Q8wQT6xZdzh0YMHjsMZPVq12qqvRxDAxo3txZZyl27hT09goWL1asWHHsyRoY0GVWxymQTmfo7RUsWrSo+BkqBd/5jsX27SbptCbhj41pqbKHHzZnnAQFPcATicCaNYpsVo1njR7V1YJIRHHzzd4xoOdPGTvOGHfcUTupGrJmjeYaFgr+56VYvVrxy18GePjhCFIqLrxQ8v73e9i2x78Hn8ccnwqWBclBMmzv281iq6aYqczUk1JKkcvlin0cz/OmzQz94ZBSMPQ8rwiGUyclS7PDqWD4n9GWCPT78s/R1EWDr9KzefNmHnzwQZLJJGeddRZf+cpXJvnnnUhs376dj3zkIwBcdtll3HPPPZMeHxoaIpFI8LGPfYxEIsFtt902py/fycQZCXyhUGiSKonruqdE6qs0lFIkk0n6+vqwbZuFCxcyNjb2uqxmSvcF0NbWNu8+3mxhGYLrVmjB4tGMwyce38NrQ3mybg7zTT2o/ggcLIdXG8CbeF9+ydE/pX5JslDQZU6AykqF47jjGd7EZegPqmSzOqsrFDTtwfPEuAWQXvV7nqC9XbJ/v0k+PwG6UvocPsEttziMjtqMjU30B8vKBU7OwRUhDENhBsKUVRvEKtKMjoZIJnV/8uqrFX/xF0E6OpaO3yy1UkpZmcvYmD0+uagALa5dW6tIJgUPPmiya5fgwx92uesum95ebWG0bZtBb69BLAabNumS54UXSl55RfDaa5qjWF7u0t2dQynJwEA5/f0m3/iG4vrr9QSo68LhwxM0DH1Otfffvn16UTDT/dMw4JZbXO67T5/rjg7FJz/pHUNL8cO/nvwp46nDJPX18KlPufzqVyaeB1ddpYHzgQes8fKz4FvfsvjOdyyqqxVv/vt6jCt6sMed3eukxbKqBYicSzqdZnh4GMdxCAaDhMPhIhjats3w8DBjY2PU1dVRWVl5DOl+tjKpZVmUlZVNOynpS7INDw/jed6kScnTJbwNpxf4ptuXv2hYu3YtS5cu5ciRI5x33nmMjY2xZ88eamund9GYKR588EF++MMfTvpbTU1NEWij0SjJZHLS447j8KEPfYgPfvCDxONxbr75ZtasWUNNTQ2vR5yRwFdWVjaJt3cycmLTRSaToa+vD8/zaGpqKnrjJRKJU+6e4Dsn+Pvq6ek5qSxPKcVTB8YYSDu0VQS5pFUP3fz01T4OjWTIuIrRgiC2PINt2IyoOLIphcjYqJEI4lAFZi5wjI2QlNpHT5cwfZcDfZyWJUoI7ArQN/mhIe11d/CgGHdn0AorhqGwLK1iEgjoIQ09lKJLdZoXqBVR4nENEn4/rrLaIJUKkcuN9wdDYZYuy5BMlhEIGPgat1deKVm6FECP4l90keC550y6u21qavQU58KFBQ4eNEmljPEhDH08qZTH448r2toUy5frXtv27UaxHGqa+nchFPfdp4EilcoxMlKgUAgzMGCTThu0t+up0G9/W/CVrziEQrqEevToRFk5END/gkFxTMZWGps2CTZuNKmrU9x6q8fy5dPf2B3HKQ5dNTU1Y5pRZqrIr1ypWLlyQnngmWcmDuDQIejv195/jiN49LaVfHBzkq6aPsLK5oPuamqjFVCyPvOnFf1+1ODgIK6ruYI+cOVyuWlNZKcrk/ox9UY/26Skv2+Arq6uY8qkwWDwlC9e30j9RL/EK4SgqqqKdevWHfc+brjhBm644YZJf/v4xz9ePK/pdLpYevWjtraWm266CcuyqKmp4ayzzuLAgQP/BXynMqZeZKcK+PweXiaToaGhgcrKykn78v29TkVMdU7wy1AnK1T9k9eGeKFHjyU+vm+Eh3cNUCPy3Ls7SV8OvPGsIl5waVuZJheycJImKh1ANWQRNWlEZyPu4LEyUbqHNzF56Yc7g4C+30PT5VBR3IbnCfJ5LRVmmrqM5zgUpc90KVVwzz02ti0pFExd5jQ1CPq9t1jMJRQyGBkpp71dFYduOjok+/cbPPusYtUqRVUVnHOOBozNmxWZjC6xlpXZ2LZgzx5BOi0wDEU06pJOS556SiClS0ODS0eHxPNCxTKt79n3u9+ZeJ5DKpXh4MEI8XgFK1YoXntNS6Z1d2tvwo4O6O8XLFyo+MY3HG6/Hbq6NGdvwQJNvr/uOrcIhlPvoTt3Cr773YnF0P79gn/6J4fSe49SiuHh4aIweTq9gC99KcDoKDQ1wac/7TBXhf7ccxXl5YyXYTUQ+wOahbxB2yMX8NmbZr42/WlFf5BFCEFLi+4D+qXO0dFRCoUCgUBgUmY4ExiW/l8Kij7/c+q+/VJqJpNh0aJFMwpRnypJttJs9XTEXBObr9dE53nnncczzzzDmjVr2LhxI+eff/6kxzdv3sx9993Hd7/7XdLpNHv37mXx4sWn/Dj8OCOBb2r4U54nWnKY7zDJ8YCSVApjmi/DTM4Jc+1DKsWGQwmGMw41EZsrFsam3X7nUIbdwxkOjOYYzRZAQcAyGMnpXMxVoKSiOmTQWG7RXZUln44QKHMJVjiolWMYDR5qQwuFSeA3HxWN0lW6GFcxUdMCo2FoioTfX7MsrZ6Sy+meYCqlgUEpc9yBQX8euZxHPi8IBhXJZICxMRgdhdpaj7VrJeGw1uLs7YVk0uKppxS33uqyYIEeOOnr0yXFhQsl558vicUEsZjBrl2QShmEwybZrEVLi84++/psRkfztLfniUQKHDzosX9/Gc3NHskk1NQUqKiI4Lqa79jbq4E9k9EuDsPDgpUrJXV1+txEo/DDHzpFkDZN/bfeXvjbv7UYHdVC2h/9qFuUOduxY6q6jWDXLsGb36y3mclk6O3txbZt2tvbCQQC3HWXXew59vfDvfdafOYzs1v8xGJw550FHnrIQkrBtm0TPojhsGLRotnLh77F18DAABUVFSxavIgNVjfdIsGqaB0XqGZgQgkmm80eA4ZTJ0qnkumn6pOWZomlC0chxKxC1L7UWDweL+77eF0Z/O1NBeHXM+ZyoTnVlkR+3HzzzXzuc5/j5ptvxrZtvv71rwPwj//4j1x99dVcfvnlbNq0iRtvvBHDMPjMZz7zug4bnpHAN/Ui8y/y4wW+Um+8ioqKOVVQ5gN8OcfjF3tGGMo4RGyTty+uYEEsxFzOCaXvZbp9PLF/jL0jWQwh6EsXKHiSqzsma9t5UvJcd4JXB9KkHYWjwDbAUgpX+X4J48dZkGzvTZFBQVsCB8hLPeaSrR5FXZaD3nI4Ug6HqzAMfRP1KQmTOXhTb4hiXJ1lItObfB71YyMjBhUVomhRVF6uWLxYMjqqFVN8Urefjbmu9v1TSlAo6BJrKKRLoy+/bDA2pvuKo6OqOPK/bBk884zJ5Zd7PPusWeyJ7dxpMjoq6e0VtLRoLt7WrZKBAV3ea2/X0mv19XDOORa33SbIZi3+x/8waG52AIdAwOLIkRBSZlBK0dCgOHQoSDarz0kioTl/H/6wSyk3VwiYajv2ox9ZxSnZ7m7Bj39s8pGP6CpGc7MaXxzo55qmLplu3Sp55pkMhpHlAx+oo76+fPz6gY0bBX198fEJNQAAIABJREFURvH17e36M3v6aYN8Hq66ShKaRvt50SK44w4XpeDzn1ds2GBimrB+vcub36yHan78Y4POToPqasVHP+oRDk8IskspaWtrIxQK8f+bL/GouQ8hDB5R+7jVPYe3y8WzyqKVAlI+n58WDGcrk/qDNL4839S+4Uz7LuXQ+a4M/lh+aZl0un2/kdzXX6/jCYfDfPOb3zzm75/97GeLP995552nfL8zxRkJfNPFTHJi00Wp+WwoFJq3xNh8SqrPHE6QyHsETANXKp48EOeGDpe+vr55qbvMBK5HE/lihmcIwdFEftLjLxxN8LVnD7OzP0V8vBqrAFdqBRd/i3qcAxwJucL430t2Fw4YEJAQlFCXgeqc/rmrhmgUamoklZW6HLZ3r2Cqu0Jp+MMapgmepydDNX1AD7m4LjQ1KRIJLTJ9xRU6Y7v3XkilfPFqRTDojWf1WqhZygliuZQT3L/Vq7XR7PbtZvGmPjCgWLpUEo9P9NAGBgSdnTqjHBsz6OlR49OeukfoeXp69YILFI2NivXrPSIRkDJFJmNgWQFCoXJiMYM1axR//McgRI6f/MTg8GELyxIEg1rwevlyh8WLE+Tz4Rk1GpXS069+CKFB24/LL5d0dXls2aLdNK691mHXrhTf/rZFMBgmEKjiO99RfOlLOqP79a+1ZJu/rSNHdIb8V39ls22bpls89JDkX/7FYaYZKiHgzjtdVq1SRKOK97xHIoSmbNx7r+5rKqUz6Dvu6GV0dJTa2smTo8+ZRxBiXHdTwDPmYd4upy9/zeSM4ANhKRj6CidTwdDzPPr7+0mn0zQ2NmKa5qSMcCbnipk4dKXqKjNJsvn9tNMVp9uL740a//nf4Txjvp58vqSZEOK4VFD8fcyV8WWciccdx6FvKMHR0BiNjY3zUneZKlTtR8Q22D2cIZ53qA7brG3Qx30knueurYf59d5hhnOKnD+JOf666Y5WTvm773kggGxBosryMBLSdytLwpIRIn3VVFUp/tt/k/zZn7n89rcm991n8uKLYppSpsJ3UlBKZ26lvSshtJGrT2ewbUUwqAHnootcfv5zg3RaIYQa7+n5oOefI60I438UyaQeShkb02VB39nczxaj0Qm5se3bdbaiszIDy4LBQUF9vQafUEgTwJWCs86SXHmlR2Vlge7ufvL5PEuXLqKrK1ycem1vlyxZYgBhvvAFSCRMurv1MVmWZPFij3Q6zdDQEK7rFm+wfn/rF78Is3GjyauvCmIx3Q/0PL3d4ucj4MMf9vjQhzzyea0Y9G//VklZWUVx9X/ggB4EqqyEsTGdufX0aAUdTYVQbN1qFLPGgwcNfvYzkw9+cPrvzNgYfPzjAY4e1ed52zbJV77i8MorRnHyVCmPl1/WnL9S2oYfFpMzE1MdXyYihJgWDEszw2QyWczwpJSEQiEaGxuJRCKz9gznM1Hqf1ZTJdn8/fuSbEopenp6jiHevx5xut3X36hxRgLfiXjy5XI5+vv75+2NN13MB/haygMciefIZlJkMhla6ypYurR93uWHmUqdAK8OZXCk5GiywLrmMkZSOW544FUOJQqkPJ3FwQTomYDHBLAV9zHld/9ng3FADCpoTIOhHzGcEMGyAqZpMDhY4LvfNdi+3aC3V0yzVT2dqW8sEwMthqEzPc3TM4jHFeeeK8nnFYGAoLZWu5lv3uzfwCTxuFUEzlBIFSkAfpantyspL9fKME8+aRaJ76AHNWpqJK2tenLyLW+RPP+8dpdIp7WmZ2OjBuH6ejXONRw/FwZcf72D645w8OBIkYT+6U8b/PmfK557ThvjnneeBvDubkF1teK973W55x7dI4tGTd7//gAtLS0ARU6af8PesCHJz35Wg22btLSYdHcHsCzJpZfCtddOXvxIKRkcHCy6cbS2VrN//8QNLhikmOVedpnk8cclCxYYKKWpGtPxDme7lB980OTo0Qm5uU2bDHbuFFRWqvEJ2AKe59HaatPW1jrtNm50V/Bd60UKQlIhg/yRd9bMO5xnlAISTEyx5nI5qqur8R1Zenp6ME3zmMzQsqxjeob+/6VgOJ1Y93SEcn9R4zszjIyMHOPXVyrJdrJxuk1o36hxRgLfdDFTGdJxHAYGBkgkEuM3jPl5400Xc2WVUkoWBXIcdEfIhEM0LmjlysVz2wWBHl751Z4RXj6cIhzMc83KICtqJkqiB8bynNMQHbdqgR09o/zL5n0cTiqSDkw3a2oICBiQnXLI9niZShPBJ7I/gQbLAkC5A0YKUgGMdABXGBg1OXYcgRdfFRRSLmNjFp5XurqcADodOuPTGZ7W8ywFrf5+QVub7oONjhps3+5RXp4mFAoyOBieNDmqe4VaYUUpneEJoR3QYzFBT4/mC2odTcXYmH5Oc7PkkUdMHnnERAhYuhQaGxVPPqknSR0Hzj5bUVamt+Vnjw0NBXbsOMzixdakbObllwVPPKFtkZJJxU9+ItiyRfP7TBOuuMJj5UpJT4/BxRd7rF072UUkGo0Wqww7dhhUVprj1k8ubW05Vq4cZuXKON3dE9OOnucxPDxMWVlZkZP33vdKurokBw4YBIOKG2/0isDX0gJf/rLDY4/p/tz113tUV8PDD0teeUWXOpubFddeO/O1PB0NTkrF+vUjdHaa9PdHaWkJ8qlPzbyNq+Qi1hTqOCASrFDVVJyAoezMx6cdUQYHB6msrCwq45Q+7vftSrOz+YLhbGLdU/djWdak8f6pfn3TSbKVguHxLMDnY0L7X6XO/6QxH0++k/XGmy5myipLe4bhcJj3XLDsuG2JnjkUZ0d/epzcrXhs3yg5R/cKV9RGsEzBSMJl71CKvmSOclswnFfECzDdrJ4AHAWOd+zfbROqbUFPRlH6sGIc9NDZn4p6mHYeKxske8k+suUWmd+0kDgaxjYF+VxpCdOfrFNTBlp0Ly6T0QCo+WqaC5dKCY4cUbzyioFpSioqPFKpKAsWGPT3M87v0+FLjAUCesJw8WI9PGJZmloQjeqMp6xMy3cND8Pq1R7PPGMSCunn9PVpbcumJmhp0WXACy+UBIOwcKFHMKhz5EwmSzicZ/XqWqqqJgvpfvWr1vh70bzG117TlIWLL9ZA/I1vWFxwgR6u+c1vTGpqdI9uulizRo4DsuallZcr3vUum+rqWp55xqOz0yEWS3DBBXEMQ+A4DsPDw8VS6Ze+pIjHBeHwRLbnx6JF8Kd/OvnD//rXHX71Ky0Y8K53zaw5CvDe93ps2GDS26t7rW9+s0Nl5WFc1+U732nCNMG23Vm5hwD1lFGvTlyMeLrwlZpKB2mmRml2Np17RCkYlvb4Son388kMHceZdthuJkm2Ur++gYEBlFLH6JPOJsn2H92E9lTFGQl8MLMn33ynJ08kpit1+qPkSqnj7hn6cWAsx/07B+lNOSjP4exKm+7RFAVXEbYNXuxPU2PDd7sGGc7qm2raE/RmpjlGdAY33eC5AEwByoNeZzLoAZN+j9qQdsANSNzmMchZHDUVlMVAhjCEGHdu0P05vwSpJ+SO/eL5pcpAQINfIKAwTUVra5Z4PIDnGeRyQTo65HhfTlMhvBJFmcpK7YCeSIAQkvXrJf39Bi+/bNDRocjldMa2Z49gbAwOHbKIx7XRbG2t3n9Tk6K+Hi691KNQ0KDR0CBZvlwRDo/R25unsjLMzTdHqao69gZTKGhtUccRxV5iPi949VXBsmWSTMY359Wl3a4uwbp1E0M9pbFgwf9l782j7CrLfP/Pu4cz13hqODUllRRJyEgG5oCighdohlYbRRS7Fbgqem+rzVV7/bw2i9vd4nS71WvjskXtxgEVEFEmsUEZBExkDATIVJlqHk6d+Zw9vL8/3tqnzqm5KpVUNPmulZVK6gz77LP3+7zP83yf7xduvNHmkUdUNnrxxQ4NDfDLX5r8+MfKed4wYmhaK1dfrRbsZ55xuOuuAKmUQ0dHkr/+6xSFQoBCITipdJeHvj41m3fFFc6E45gMtbVw220FHnpIw3ESbN7cRUVFdEFF2ecKTz5wcHCQaHTuxzJVMPTcI0odJEpLqqWZobd5Lp2bbGhomFXPcCq/vtlKsnmvf7LHdwIHvvHwrImGh4ePihcfjAVb72bp6emZt3NCKf5rX5zqoElfxsZ24PmBPPWVQUI+VZPc1zvEc11JQobOEDa2C72ZyWeqZhJq0uWo68IMj0uW1k59gG6DrUFDCvoiuCMhhCYRmqB9uYOQGq2tkj17BHv3lr6SyvA8x3Rdz7N2bYZo1OXQIYODBwO4rk5FhaCpSWV2g4OC5mbJ8LCaBYxE1CCG46ixhtZWycc+5nDFFS6u6/DTn+rs2qXGEPr6YN8+weCgkiJLJODwYcGePYo52tIiuOACh61b1RnYvx++8hXYvj2HECYbNoRJpw2++U3B6tUO7363W5bVrF/v0tWlzHc1TZVpQyGlI6rGFMbIMbYNjz+u8cADio151VU273xn+Zkfr56STqd55BGJ6/oJhyNomsZzz0ne/34D04xwzz0mtq3GOPbvr+CZZyp4+9sTxYqDR/AoHQ6/++4A995rjJJxJDffPDWbsxQ+X5YzzujGMAxisfYF20DOB7mccpzXdb04q7gQKM3OvHLlVMEQKAahTCaDpmksXbp0QnVnJrHuUkwnyTaZeS0ooWpv8H78mnMy4zuBkM1mGRkZwbZtWltbj4oXH1CkLnd3dzMyMnJEzgmlKDiShrCJ7Uq6hm2EdNnYEGIokWJHTwKpGfTbJknHIu+q4DZV4ApokJ3ilxKV1RWYOUBOgA7oLiwdgb4Iji2gJodmOlSsNDmzNcLBA4p9GQwqG6OxhHyM2Qkmy5Y5nHPOCN//vrIjCAQs4nEdTXOor3cBE11XfbNEQhAOC/r6BPX1clQJRnDgwJgY9tVXO4CD48BnP2sUWYdjWagoZpqZjJIb27rVxbIsfvKTHPG4McoCNLn9dvV+Ukr+8AdBNGpz4YVjJ/Qzn1FB6re/VfOEZ5/tksspRugVVzi0tkq+9CWDnTs1LEuiaaI4Q/eDHxice26BWGzi6fXcRtLpNNXVy0mnx5iM3rqaSCi2qPdvw9BIpYLU1Y0FgQMHHH7wA0ilHDZuTNDR0c83v7kUy1IBWgjBnXdKPvShyVsGoLKG/v5+EonEgnlNzhdeyyIej9PQ0HBEG8zZYrpg6BGMfD4flmWxf//+Gcuk3ueYSzCcTJItnU7T19dHJpNhaGiozMk9k8mQTCbLstKFxiOPPMJDDz1UHF4vxU9/+lPuvPNODMPgox/96FEVqIYTOPAJIYp+del0mkgkguu6EzTkFgpemcVTjJ/OOeH5nhQ7B7IYGrxpSRWxyOS704FMgQd3D/PaQIZEwWZNfZgKEcDvZHl0Vy+dSYf6sJ8ltUHSqQypnD2hPFkKE6YVV/Frakh9vjAFiKYshZV90DICNXnc1gSvJiKcbq6gc78PTVMswmxWCVVblott66NBT5Ujt22rpKurGsvSsG1FtgiHXTZutInHBYmEjetatLfD/v0BqqslmYyO42hYFmzePNFyJpuFhx7SOHxYUFmp+oCeZ10goB6fTCqR6PPOG1PP0fU2KirUwj4wAMPDiiwihODQIcFTT2llga++Hr7yFZtEAv7t3wy6ugTRKNx4o8WWLSrjDAbhjDMkb7yhAvTgoHKutyxRZJJ6KCVpVFVV0dHRwfvep/ONbyiCDkhOO80hl4OaGkXM8Wb+HIcytmY+D7feGigOwu/dG2Hlyka6ugx0HQYGJLmcRXf3ELt2DZYt1sFgEF3XSaVS9Pb2EolE6OjoWNTsIZ1O09PTg9/vn1Ro+1gin88XM86Ojo7ivV+aGY7Puksz7yMNhrquF8ckmkb9q0qd3H//+9/zn//5n/T29tLR0cH69etZs2YNl156abGseyT4x3/8R5588klWr57IzO3v7+eOO+7g7rvvJp/Pc80117B169ajWiE4YQNfX18f/f391NbW0tzcTDqdZnh4eMHfp9Q5wefzYZom9fX1Uwa9NwYzPNY5gjkqsXX3awPcsCmGT5+YFf7nS/1s606Rs12SeZt6n87ewRG29VtkHYHjSoatAvVhk4gpyE2vOIULFKaJjHMNeqWhpcYPEZ9Jb9rCv26YfF6AXxE68uER7k3tQjstgjXkx/pDDKdgoOtKlWV42B1lcwpcV0cIjRdeUESU2lp10x88KIhGTQ4cEPT2ql5MU5PDBRfYjIwoD7p8XjlC7Nghectb0qOKLgESCZ3vfEcpn0QiarB+3TpJKOTyyiuCvXs1kkkVKBIJSVPTQbJZl/b2dv7yL/18/euqLJnNqgBcGlQNY/LcuLJSZX/JpAp03uXQ2+t5DCqW6KFDypUCVBm0VFzaK98JIcpIGqtWSb70JYvvfU/n6ac1HnxQubX//d9bfOpTFj/5iUE2Cxs3urzpTWNf6sGDyjYpnxek06oMu22bTnW1IhLpuiCT8fFXf1XHsmWVZQu2p7qiPltlcSN5LOW4PDiOQ19fH6lUilgsViSILAaklAwMDDA8PDxpxjneSgnG+nYeiWV8MCw1+J1LMBxPbCmVZHvHO97B5Zdfzs6dO9F1nd27d7Njxw5Wr17Nxo0bj/g8bN68mQsvvJCf/OQnE3730ksvsWnTpmKWvGTJEl577TU2bNhwxO87FU7YwBcOh6mqqioGoIV2aABVQu3p6cG2bZqamqioqGDPnj3TzvIdTBSKQQ8gY0kGMjbNFeW7H8txeaE3hetKDAFBbO5/o5+uLORGsyMAHJeH9iUxBNNme94zFkqsyABqAwJbaDSFDEDg12EgY1EQQEAW39N1IWFkqQuEcKoKuCsHMV5tpKZGSYIFgw62rUYAGhqUMLXfz+icnQqe8bjgF78QJBIafr8aag8ENGIxk74+jdpaxcrM5TQqKy22bk3zzDMOd9/t0N/vo7MzyLp1DlVVOlu26Jx7rss73+ny3e9q/OAHgpoaF58vT3t7lurqWtraVCmxo0MFlOef1zBNyfe/b7B3r3KLr652ueqqqb9rIWB8geGUU5TmaCYjqKlRPcH2dpfmZrj6aptgEPr7Hb76VZvubpeOjlb+7u8MAoHy4KJp8PzzWvH/R0YEv/iFznXXOXziE5PvgOrrJYODSqlFStUPXb7cZe1ayaFDKvCvXeuOZoljRIvh4WEymQzV1dUEg0Hy+fyEvtZ4q6GjBW+T6Y1uLGbGmcvl6OrqwjCMSQf0p8JUfbvSGU7P4Ncjr3jnd6pgaNs2g4ODmKZZtv6MZ576/X7Wr18/L1cGmNyS6J//+Z+59NJLefbZZyd9TiqVKgv84XCYVCo1r/efLU7YwDfemmi2yi2zwVTOCbN5n5qAgeNKdE093tAENYHym7c7WeDxAyO83Jcmbzn4NZfhrEtPfrJXHM3kZtGUG6/IMh/4AF2DKhOkptMcMrFdl2TBJmRoaJrAdsYOxq9UxAhqBoGGAu6+EMkhPzoO8biLbZtIaRAMQjjsjTzAkiUug4OChgY5KtmljGCV27siwwwNje2ABwY06uslwaAAfHR2xti+XaeyUqBpDnv3auzbBytXZrAsh+HhNF1dDps2hdi+PUShUCAQCFBRUcWWLeWBIxaDSy5RZ27JEpt779VxXXjrWx1WrpxbNzQYhJtusrjvPh3bFpxzjsPmzWO790Qiyf/5P4KDB8P4/RF27hR8+9sOn/xk+TVlWZTpc3r/Nx2qqlQWretq5q6yUtLQ4LJrlxphaG6WXH/92Gf3Mk5N02hvb59A0vAo+NlsdgLJozQQLkQwtG0l65fP52lubl4QP8r54mj0FScLhqWCBqlUqmjjVHpe/X4/+Xyevr4+ampqqKurK2Z/4zPDrJIjOiLOwWSWRDMhEokULYtAlaiPdpZ+wga+8ZhJuWU2KHVOqKmpmeCcMJv32dwUYSBj8cZQDlMXXLisiqA59hqW43LfG0O83DMCVp6hjJq3Sy9ssjpvFIAaU8MSgoAmMTRIWZLhvMNAxlYlSyCsqcdWBQzqQzp9gzaDIzZul5+wq+GrK9AbF9hBB5ELoobPGV2MJT09GqtXuwwNqQX5zDOVG/nQkCrLeSoqpglveYvNgQNKxSUQgFNPdTl4UCvO+TmOTjAI8bgP0zQ49VSXq67SyWRGCIV6ufjiAM89F8E0LS66aBjXNcjlgpN6s61cKfn0p2eoKaPIO7mcCjbj18SmJia4qXuWV7Ztk8mcQjA4dusqMenyx0cias7v5ZfVELquS84/f+brOxbzXC8UmWjXLp22NmXF5Pd7wuFjKjDTLeylFPxSksdkwXA8/T8YDM5JN7evr4/q6mqam5uPqejzeHj+mKZpzinLmw/GCxrAxGDolUl9Ph+2bTMyMjLBPcLjH/T19c3aVWIhsWHDBv71X/+VfD5PoVBgz549rFy58qi+5wkb+BbSk28us3+TzfJJKUkVXHy6wG9ovL2jhrd3lP/+tYEsOcelxnB4obObu/bmSDnzYFceA8RHm4ECGMxmMXWNrCXLjtUCgoZG2NDQUAGtsibF8L5qWqIOr+1zMXWVjelaBnmoFp9PUl2tLIPOPFMJUoMaBdi3T+lJJpNjSi9btrg8/bRGQ4NycXAcaGhwi2ME+/Yp9ZNkUs2dnXKKpK3N5cYbcwwMqOHk5uZmVq2q4MILXeLxAj5fhmw2y9DQ0ATH8GBwaiHpUvzmNxo/+5lOoQDLl0v+1/+yJ3U6gDEHEM+GKhqN0tambJA8BZumpokBTQj42791+PWvJSMjiizT0TH11dLdDb/5jU4k4jI4qGGaqicaiYyZ3QJs314gGt0/pTP7TJgqGI6n/3d1dZUNhnvnuPT9vM2A4zhTDqIfK5RmeYvJZPWCoafWU1NTQ21tbXHwPpPJFN3u77vvPoaHh1m6dClLlizhnHPOobm5+Zgd9/e+9z2WLFnC2972Nq699lquueYapJR88pOfnLOAx1whJhM0LsHxuK4uCBzHwS5RR5ZS8sorr7B27do5ffGeaLWmaUVx2+mw78AhXhh2MUMRllT6ObUuyE9eHWAgY2NqcP6SqqKAtHdcP9s5wP54jkw6xa6BFL/Yb08qMXYsUNTjnOdzJWO9RJ8GYUM5QERMWFYTJPd8jIGDPhKDBrm0AL+DXp9Fe2oJUqpMRghF8tiwQZJMwvbtauZuZET1C5ctU+7pPp/S7ly7VmKa8Oyzgv5+lWVJqXqEAwNKBq21VXLBBQ7Dwzk++MFOli2rIhqNomkaTz2lcddd2qjXn+QTn7AJh8t314qIkGNkBOrqDCoqSjMXk4cf1kfnA+GBBxRLculSNSLw1rfavPe9E8+qx0r0+XzEYrFi9jA8DLfdZjAwoLwHP/Yxh9muE0rqrdzaqL8fbr7ZJJPx7KAkl17qsHq15CtfMUdHOiTZbI7LLhvgfe+LHPVS4vhg6GWIXjD0SnO1tbXF8t1iwROh8Pv9xGKxRWWP2rZd1BRubm4uE+guheM4vPDCCzzxxBMcPHiQQ4cOceDAATo6OvjOd75zVL3wjiGmvChOZnwl//aysdk0xHM5pXJfKBTmtMP7zaE8I5ZLIg7PHE6wtNKPI5V7wkDa4mt/OMQH1jfypqVVaEKwP55lx+Eh0uk0/9Vj8UbcPeI+3GwwXojaQ9CA9MyVvElR+nqjrT1lb+SCVQBruIC//TDZRJSACCMjElmfgaQPV0pCISX1FQpBd7dg/XrJ4cOKhh8Oq/+3LIhGVZ/KttVohBKQFqRSglBIjhq+iqKHXzotyOVcXnklw4EDflx3FVu3wrXXOtg23HOPuh4CAeXEcO+9Ou97n1NWanr9dcF//IdBPC6prLR43/viRKNJ+vr6uP/+Sp55poZMxuS11/xkMlBVpY5n0yaXZLL8utm50+G55xIsWRJn48aGsn7Hs88K9u4VXHSRwxlnzG1fKiV8+csG27Yp8ek3v9nh4x93ePJJjUxmzIZI1wVLlkg2bZJ86EMWd9zhkkzmOeccyXXX1RdNfY8mppqF88p3Ukp8Ph9DQ0PF8t14lZSjDa/kWzqvuJjwiD2VlZUsW7ZsypKlZVl0d3cTDoe57rrraGhoQAhBLpdj//79CzK+cLzjhA18k8Erd04X+LxBYU/lvrZ2diLSAAXHpS9rs2Mwj+lXpb/XB3O8tb2KPYMZHj+QRCK57bluXhvM0BaER17v5bfdeQ6kJZkj6uO5zJazaY4+evzb+WHKkQjPyWE6eJmeBhjaaPYnBEFdvd9I3qEuJAhu6iNgGsSEST6tc3Grn0cPSeJpB0u3SKZ81FUJ2tpcli+X3H57qbQTvOUtLuee647aCsHdd+uMjKjfV1QodRdNU3+rwOeSStns2BGhvl5llM88ozLHzZuVVJnXqhGCUUuictxxh87OnSClhhA+Hnusnk98Qhn9DgxoBIOS7m6BEC6OI8jnXQYHdZ54QpLNuqxZ47J1q+Duu7P86Ec6pllFOFzPjTc6nH66CnA//7nGz3+uBuwfekgpuVx22eTboJ4euP125ci+dKnLhz/s8MQTGs8/rxXLlk89pXP22S6hEEXTXlBZc1WVmj3r6Ojm5puhsTFGMBhk717Biy8Kli6VRdJN8fuVcOedKrNdvlzynvc4M2pxzhalpcT6+nqqq6uLSkiWZRUzwsnEpL0y6UIyPDOZDF1dXQSDQZYtW7aoWZ7nI5jJZGhpaZmy6jTeR3TNmjVl5eFAIMCqVauO1WEvKk7YwDcboepSeHYlAwMD8xatNjXBcF5iORKfAKQkYmp0pwq83J9BE+BKwVDG4s7nuzijQefxPpvXEkdacS61kS01EZoIAYR8KsAZqCaSI5WQ9VSKLRoQNCFrzRz8TE1ddJYER4JfB1tKTF1QZejEIj5SBYeQobG6zk/acihU5JCX7CK1I0D2j41gFFi1XNLV5eeyy+xRsov6PltbXc46y2XDhrEjPe00m8FBuOEGNacWCHiC1DaGkWfZMp1QyKDyp5M9AAAgAElEQVS7W/UBX3kFNmxQs3+hkOqN7d8/5iC/aVN5sEkm4ckn9WJAVP58Y2eiokLDNDUqKgT9/WoA3bYF2axgxYoCplngW9+yqag4wC9/2TbaAzOwbcH99+ucfrrabTz1lF5UlRFC/XuqwPdv/2Zw8KA2ejyKvBONSkr3aJqmxkAuvNDlhRdcXnxRiQGcf75NQ0Mv+/eXB5lnnhF885smjqOC47veZZeNa3zjGwb33KMXvQYHBwUf+9g8ywMlKC0ljieMlGaGU4lJ9/f3k8/ni8GwlFE612Doui59fX0kk8lFnxEE1Wrp7u4ujm9MtQn3WK+pVIqmpiZisdiilocXGyds4JsMkzEu5+u2PhmEEGxuDNI5ksVyJZWmzpr6EOvqQvzhcJJUwcZ1HfIFSdoRpHoFO4ePfOGYKD09feaXLKgMTmgSV6pgZmggpApY41/ZEKqsKLyfJ4mOGkq5JRbS0DSDoZyNjoslR50KHEmlX6M2YLAmGmJ/Ik9NUMcF/KYgvDSDKTOY2QDVpo/uAZM/DAkGBgxOP93hgQcMCgWVdXR2ClatUv0zUJT+3bsFH/iAza9+pZNIOJxxxgirV9vEYtVkMsorbnhYfY50WjkmbNigZgQ//nGb++7TSKUE69e7EzKd555TOp5qIF6VW01z7DHXXGPzjW+YxTGLmho1mF4owKFDfkZGNBobM4RCDQSDIXI5SS6XHyW2FDh8ODG6M4+Sz5sYhpg2k/KczYvnXlOD8Vde6fDggyoLlhJqaiTnnKMy409/2h4dlk/jul1YVmBCkHn4YR1vX6hp8F//ZXDVVYXi77dv14rH5bpw++06Tz2lUVsr+du/tVmxYm4bOE/6LJlMzqmUOJOzQjabLRrQGoYxYbRiqmCYTqfp7u4uEnsWc0awdEh/pvGNRCJBT08Ppmly6qmnLrgG8Z8iTga+Eoxndi6Ec8J4/Lfl1fSMZPBVVKAJQaVPI1WwaA3Ca30FbCnQdI1qv8H+ROGYsItK+3my5E+9AUMFRUJBQMEtJ6h4MITK3LKOesz41xZAQIfmCh+DWQvHLeBIQBNEfBr1IZOGsEnekWwa9Qx8a3sVjWGDR/cnAPD7INbikunO4+wNkew3iVQosedt2wxWrpS89ppgzx6dn/xE8PLLLp/9rI1hwDe/qXPggIaULo2NCW66qYvlyxuIRKp54w2NL31JZ/dutUA7jpIGu+46u8iCNE1417um7qxWVKhB9lBozLH9bW8be3xbG9x6q0UqpXqRL70kuOEGH319Kkr4/QamWUlHR4BLL9W46y4DIUDTJO94R5Zw2GF4OE9nZ57t21WAN01Yt87iy1+WfOITEtMsL/cKoTRFHUcxZs87T1JfDzffXOCBB1TmeOWVDt5YmOPY6Hof+XyGxsbGeWUyas5SfabeXuVyn06rc/K1rxn8v/83e0qW168Kh8MLEmSmC4ZemdQLhqZpTpgxHBwcLGZLpbN0i4HZBmDHcejp6Sn2IBd71ON4wgkb+KYrdXo06Ww2S0NDQ7HUsxDwmzqXL/VTqKji8c44v9w5wAu9KVorfIT9JvGcS8F2SUiL1Pj0at4o90jXcJFo0wZVCfSNZn6OhJAuyDtywnM0QGgQ8OkkM04Z8cazMYoYKmu0bIdEXs32aUKVOHUhqAuZxYB30bIqKv0Ga+uD/G5/glcHspiaoKXCJJl3WHt6gRd3S/zLExRac/S4OtmD9fT26riuyoQsS4k+79ihJM/27tVw3Ry5XBafL8CBAx2cdpqao/vOdwxCIRVkuroEp57q8oEP2Jx33uzP/aZNkhdfdLFtjbo6yebNLmefXf58NYOofn7wQchmbfx+HddVXc/aWjVmccUVLkuXWnR2CtaulZxyigFUc889Oj6fzrp18PLLAtOUVFY6bN9u893vDnHRRdkSyn+QbNbE55NYlsru/H51PM3NcP31Y5u70jm4qqqqactll13msHevoFBQ98Ill5RXIz7yEZt//EeTnh6Bz6ck1jwMDs7u/vFYidls9qgPopcGQw9SyqLnnWcCWygU0DSNcDhc5o5+rDO+uZRZvRKopmmceuqpizrQfzzihA18MNGTTwhBPB6np6dnwZwTxmN3vMC2wxny5gD/d1sXiTzkHejPF5ACNASWLYkvaK7n5V0Aam7Op0OtX9CblQhUAMtLFahKS5UOgIT0FEHYBgwJybwzocQpR98nL6EhqDOQdTBGT6epqSzQcR3ylsWLPSn+anWUaMigwqezayjHbzpHqPbrvNKfJWM7nNUcYXNMpyOQ4eHXU+hCqcCk1x4iO1SHPRgmYKgemuOoTCybzZBIgN8vRpm3OobhAC69vYJEQvX71q+X5POSs892ufjiufFmhYC/+RuHyy93kBLq6iYOpcNYn3h42IfPFyUQ0EYzO2hoKO1JyjLndYBMhuJj/X71ffp8QXw+iEQCNDWp+UI1QD3CwIBGW5tA13U0Tae/f0yz0UM+ny9qbLa1tU1JffewZYvkn/7J4sUXNdrbJWvXlh/jaadJfvjDAoOD8Mtf6tx3n+c9p4hC00Gp0ihdypkC8NGEN0jvWQdJKWlra8MwjDL9zHw+X8wMS0ulR+uYveqT126ZKui6rktvb2+RBHQ01rA/B5zQgc+Dp1wwMjKC3++f1jnhSPCHwwnueLGfPf0ZXhpKMlwYI4N4Tucm8qjP6OkC/IYg62oETRdT09A1QTqvvPo8+6FSeJxQb/kq/TnnTN019GsC25UEDB2Bg6Fr2I6LLQVpS1Llk+wbzhHQJP++Lc2zDUEMzUA3DPw+g6CpYepQoelU+Q0OJiwql7o0xF3eSGTISBt7g4Um02hPN9Lmq0QIgy1bLILBw0CWjRuXsn+/6mtUV0suuEAdeUODpKJCZYj9/WLUoHZ+Gw4hVMCbCmoH3otp+nj3u2vp7RU8/zykUtDe7vLxj0/fyz3rLJenn9YJh1X25verOUUhJKefriTAvMDV2AgrVuj09jJawbCprh7gjTfiRdUO27ZJp9NEo1Gi0eisKxqtrYpANBV8PqU8c/31DroOu3ZpRKOSj3xk6s/n0ett255VAD7a8MqsFRUVZQG4lAFZmhl6tmZeMBzfMzySwFOqkhOLxabtc3pMU4AVK1Ys+njF8YwTdoAdKLoWe41f78L2bDvmi2cPJ9jWncKVcFpDmDcvrcJ1Xf71id08si+O5Ui2D7ijYtLHHh4JxdQFBVtS4ROE/QZZy2Ek705KTvGeB2MXhdcb9DLG8cHSACoCGkIKCq6LJsAZXTNVMATbFWiaUGxPAW9vj1BlwKsDWZoCMGxBVwaEprG1tYKAzyBkajxzOMVwzqErVcBxJUsqfZzZVIE/4+eaFX50vZ/Kymo6O+txHA3DUISLLVskpaI6O3cKbr3VYM8ejfp6l2XLVO/rzW+efdZ36BB873sG8bgywP3IR+yiUatlWfT19fGrX5k8/3wDum6ydavLuec6vPyyRl+fUozZulXO6Gy+bZvg2WfVDJ5nVnveeeUMVg/d3cq/L52G005z+cu/VKa78XicwcFB9f2NajaWLtSeVNhMgXDPHrVRWLdOMt89oqd4NDAwUFSlWUymYWmZtampac7lQS8Yej1Dzxnd5/OVnd/ZyoJls1m6urpmHIz3guPQ0NCosk/bCWEmOwucHGCfDIcOHSpe5KUq80eCrkSepw4lMTUNAWzrSuLk0tQ7CbIFh35LZ/dggdzCfIRp4Q2Jj+dzuih2puaqMqctJY4rCZo6luOStCbueLxiqSbGHNhLHyMEhA1B1lKvaQBSGcCTdxxsF4KGYHmNn8GsjXAlNpAouEhXogO2JhjIQXNjhDVmgNqAwb6hDN25LG0RjXw2TWe/xdJKg94RG6lpCCTRgIEtwXIsdCNBOFxFQ0M7//7vQfbvV+XEaFTyqU/ZjFeSW71asny5kjLzsG2bNm3gk1LN8v34xzqHDmls3y5oapIEAopR+sMf6lx/vU08PjxqSVPHc881YIzWeR9/XGPZMpft2zX27NF47DH4zW9c/uEf7GmD3xlnSM44Y+ZhzkIBvvxlk+eeE9TWqsDnuhOteoQQZbqZnr0QMGkw9HDbbToPP6wjpWDZMpd//meLuRIFPX86YFIX8mMNr8xaWVk57zJrqd6oB9d1y3qGXmY4XTCUUtLf3z8r+TMvOLquS0dHB9XV1fM7AScYTujA19jYWLa7XQih6t6MhTH6evF0lj8eGubVQ4J1LbU8P5xix2BhhleYO6ZSWfFrUBvUyduSgRL9TPBm73QKjkNNwKDKb5J3Jf0Za0Kfz0BlcwLw6wJHguNKbDn2vqYGNX6djQ1+goZGfUjnV7tHyBSUKrUaWBdU+Q1iYRNHQk/KImMVcEdnBXFc3hjKURsyObu5gr/ZUEei4LJ3OMcPX+nnj91pRnIalmHQXKnRlSywukJwKJ3H0HSSSYs3nVJDLBbj1Vd9dHZqxUAyNCR44gmtjG1Z/HzGGBtR/Xvqc/3664Lvf99g+3ZBJiNYt05ZJ42MwMaNEtuGe++Fp57KU1UV4CMfaSeb9Zepnei64Le/1di7VysOjXd2ajz2mMZFF01+/R0+DM88oxwmzj9fTjvOcNNNBg88oObpDh+Gr39doOtdnHGGMaE/ZBgGFRUVRaLEZCLSnm5mMBhkcDDM/ffX4ferzc7Bgxp3361z7bWzU1cY63MOl80ILhZKXR2mG/6eL7zzVlq+HR8MPQKN59fpMUvb29un1Pz1guPg4CDV1dUsXbp0UYfo/9RwQp+pQCBQNr6wEJ58y6sDPLZvmFQqyY6+DJrhp72hiq9v66Ynu/BiY95yOj7w+TUI+3QythqS9xdUCVMfzdh0DXy6IGjohAwdiQQpMUfrltIZC3YBU6PCp5Et2OgaFFypxhpc9QBDCHQB57REeMepdVywpJIf7ujn5f4cu4dzuBJ0IVhW5aMuaPKpM2M8eiDBc93KimQop3RKQeBKeL4nzd+sr0fXNGoCGrGIiSsFNUGDggtdGYfV0SBthsmaKo012QwraoOsrAuzNOjS2dlJZ2eAVKoBv9/AMAx03XNxn4hLLnH48Y+V318gILnkkqmvgR/+0CCVUozQTAb27NGIRCCZFEjpsmOHgxA2zc1+bNvHj34Ef/d3FvfdJ5X5LopB2twseeQRNRAfjSoNUnuKNtjrrwv+6Z8MslmlK/rKKw4f/ejkx1gowKuvjgVZx3EYGpLkcjGammZTkxQYhkll5UQR6Ww2y4EDNvm8hW0rRwVd1xgaypPJyBmZjh5Bw+fzHXXngplQSqY51q4OkwVDby7Pk19zXZe9e/fi8/mK2bfjOEQiEYQQHD58GNu2WbZs2Z+LruYxxQkd+MbjSD35XNclNzJIhxjm64dsXh2xyRYsHug8OqaKOmDoKgAFDMjaqoSpC4iGDPy6zuZYkKCp8Vx3GkcqWbCC7WLogpChETQ13rmqlre1V/GLN4a4b/cQmYJyanYdi7QtqA0ahAxBQwAqDUnCNdkTzyNdpUBT4dcJm4JToyGuXKluwqGcw+ZYmJTlkC4oAo0joS5ksDEWYUtzRZFR+6Vnunj6cJKBjIVAYOqCB/bEOaulAkMTHEjk0QQEdQ0pJZoQJHMFajSLq5ZWckrbqRMo6YFolid2Wgx3u+i5PBG/TSw2wKFD/uKi4xEPNm+WdHTYHD6sNCqnGtOSEhJqrJBAQBFTLAvWrHHp6bEJh4eJxSqIRsfKViMjkupq+B//w+LBB1V58M1vdrj7bp14XCOXUyLRW7c6vPWtk2+M7r9fJ5v1giY89pjOBz/oTOroYJpqjKCz08G2JZqmEQqZbNkyfvpyIh59VOOOOwzyedi82eVTn7LRtHJ1lLPOgi1bDN54QxXNAwGbrVvT9PYmJ5A7vBIeQF9fH4lEoqzMuliwbZvu7m4KhcJxQabJ5/N0dXWh6zodHR3FDYGXGXps3W984xs8+uijtLS0sGrVKs4++2z8fn+Z3+dJzA4nA18J5lvqlFISj8fp7e3FHwzx44Maz/TmZ5Tvmg8EKuCFTYGpawQNjdYKg6GcQzLvMpSz8RuCgK7RGDFYVx9mY2MIDcGhpEVbJcRzFs0RH+e2VrK6LsC5LZWEfDpnNkc4NRrk3l3DZAouq6LVGELSl8ggbZuKUJARW2drXZAnDib5Y3eKCp9OQNeo9OlU+scup/qwyaFEnoipM5yxyeJSGzBI5R0eP5DgLe1jHm5nN0d48mACMZrxNYZNLEdScFwMTefU2hCGNkxzhY9E3qYnmSMc1HjP+hhrOhon3PS/2hXn1/uGeak1S7xSsiIU4Z1bq1mxtLXIwvMo6V6vRWkuev2ZyRcRIRQtf9cujRUr1IxcQ4NDNBrnQx8aZO3aBh56yOThh71eDbS1ydHnwY03qiuip0fNFm7e7NLVpRwRzj7bndJhYfya5g2oT4ZcLss73tFNf38DPT1Bqqrg858vTGtJBEp27VvfMnAc9cJPPqmzdOlEB3ldh1tusfn5zzXyecEFF8CSJXVA3QRyh1fCk1JimibRaHTK0t2xwPgsr6WlZVGp/h6bfHBwcNKyb2lmWCgU+PCHP8zVV19NJpPh8OHDvPDCC/z0pz/la1/7GkuWLFm0zzE4OMg73/lOvvvd79LR0THzE44DnNCBbyE8+TzrGIDWtjbu2Z3k3j3JBTtGDwII64ogUuETvKUtzMq6CroyDq8PZgmYBnGfQ6zCZFNjiERBsrI2wDmtFWxoCPFiX5aAoZEoOLRETP77pgZW14VxXJdbf3+YrrSFX4emiJ8zYmHaq3y8qdFg16FentJM8FfRWumnKeLj5b4Ml55SQ5VP46W+LLouOTUa4Jp1Y3z+t7dX8vDuYSRqlZZSkrYcOhMFHtoX5y3tYwrwb15aRX/G4ievDlDlN2kIG7RUql4hQE3Q4LrT6rl3RzfBSps3ra/jbae2TFpWSxYcHjswwuFkgbzt4Pok/WaaX++HNy+roKqqqky5o9T2Znh4uOi0XjqfVeqx95GPOPzsZzA0JDnvvBG2bOmmri5Kbe1ShBC84x0uUtrs26dRVSW55pqJ11MwqPqKmqZsjqRUXoJT4corHV58USOVUsH04osn2hB58l6JRIKLLmrkiis0MpnCpEa3k6G/X/UsvdfVdWXZNBl8PnjPeybzABwjd3gMScdxqK2tLar/x+PxefsYHgksy6KnpwfLshbduw+Ul2BXVxdCiBl7eaVen1u2bFnUzcN4WJbF5z//+UU/n3PFCR34xsMrdY4f9J0MnrpLJpMhFotRVVXFK/0Z/vuD+47oGEI6uFKRSMI+HWu0nxYNGVy7vp5MroBPOJxXp1OjZ/n+QAZZkDhSp9on2FAf4eNntqAhaYyMLSZ/cUo1Tx1M4riSeM7mV3tGuH/PCM91p8g5koIjOZwsEAtnCeiCB7N5/qta54ObWvj/NpUPqL19eTV7hnMM5SxaqwJkLYcV0SAVvrFAlLIkdWGDobyNorVAvOAQdV0OxCdyWv9qdR2roiFeGcgQNnUuXj6WEabTacyRXt67zKSxcdW0DMCC7eK4Sgi8L2sXf/6jTJG2XEIlbvZCiOKOuqZGOSm4rlum59jf34/jOGWB8LLLlFNAIBCgsXG8aLInbzZ15aCqSgWvBx/UcRzlMnHllVM/fvlyyVe+UuDZZzUaG2XRrcHDeHkvj+Qwl/WxtVXS0iJLgp3ktNPmV/0oHUTv6OiYkFWVnuN0Os3AwACO40wIhqZpHnEwLFWmqampobW1dVHLgqUjHHV1ddOWKb35xkwmQ2trK/X19cddSfOLX/wiV199Nd/+9rcX+1DmhBM68I2/iDRNK6q5THWBeTvr4eHhCeoun3n0yIKeX0B1wOA9q2vZN2JhSyUSvSYa4MymCKYhqPRXcGZzBE0IhrI2Pa/tI0ceDZf2gEvvcJwvPZbEMEzW1oe4el0Dfr+fNXUh1tSFeKkvza92xfHrAsuVvDGcpz5oYEsVnDrjOZaGNYJ+H0YgyJM9ebYum3isL/dl0IVGdUCjOmAwkLEYyFo0htVq217l48BIgcMJC8tVLNCALsnbkpW1k/dU1jeEWN8wxqrzZuAymTH9yJlu/NqgwYraALuHstiO+h6DhkDXBLuHs9SHpidUqJ5YqIzdZ9s2uVyuaDrsOA6aphUXVW+hnsvs1FVXuVxwgUsioSx+DEMRXX7zG41MBt78ZpdSzkJdHfzFX5QHIi+LKRQKRyzv5fPB//7fBX7wA4NcDs491+Xcc+c2aFqaVU3XO5vsHHumvt6Go6+vb94zhqXH093dfVw4tIPaLHvav9ONcMxkH3S84J577qG2tpbzzz//ZOD7U4dX7hy/Sy0tOUQikTJ1F4+kkZjKrG4cfAIagxp+4ZK2IWVDKGASMgSVPp13r47y+nCeF3szNIYNtrZVsTlWvqgVHJc7XxmgvSaAiyDnuPQ5LiG/zv6sRX1A8PpghqdeP0hrwC3upruHJUI6SKmPEkYg77j4hYtlW5iayoKkgOqgieNSthGwXcnrg1mGcjaudNGEOk+aJtg9lOOxzgQCOJTIE885WK5LpU+n4LpUBwzObQ3z7jXTSJxQviuurq6eNGuYCkIIPro5Rs52ybsSgRLCbqvwo4v59XN0XadQKJBIJKiuriYajZYt1J4HnGEYM0pYPfSQxo4dGjU1kve/36G+Xo5+Zrj1VoNXXlFzh7/+NdxyS4H6+unPT01NzYL1qlpb4bOfnbsbyPjjmU9WVWrq62GqGcPSczx+xtA7nng8Tn9//3ExGF96PNFotFj6nQwe8SadTh/39kF33303Qgiefvppdu7cyWc+8xluu+026ie7aI8znNCBb7ILygt8peUrb6evaRpLliwp26m6rouUSgdxa2uEbT2TuJQCFSbUhUzCpsDKW6yN+lgdq+L+vQnMrE1N0KDab+A3VMC5KlbBVasnP+7uZIGfvjbIEwcS2K7k1LogtQGTh/YMMRwvYLuS3SOSU2sDvKephZX1weIi3exL81g8jjtKOFhbrXEgnqcvD4ZuUOXXGMpabGgIETE1VkUDxfNkOS7fer6X/oyN5bj0pi0awya6prG00s+De+IYumBbV5KMLfGbgoawD1OHSp/BmbEwH97SSHPF1KXKTCZDT08PhmHMe7BZ1wT/84wmbBf6MhZSwpIqH1tic8+IlPZlN7qulx2PrusT3MEnk7AqpaP/9reVfPvbPnI55dJw8KDg5ptVoNm7V/DCC1qxx5ZMwkMPTZyPy+VyRfHh42Hw+2gOos91xtBzXo/H4zNmVccKpVnnTMfjZXmmabJ69epFZ5vOhB/+8IfFn6+99lpuvvnmP4mgByd44JsMmqYVmZ35fL4oYeTp5HlBwHuM67oIIRBCcPOblhLPOtzz2hAJRzmZn9UUIBLw0RTScPI5anwQra4vli7+2/JqHu1M0BgxcVzJaQ0hllRNX9Z47MAIjqsCaXeqwJ7hPHalxNTAld5ws6AnbZO3bb73cj+ZgsuSKj+Xr2jjfzbEeGLfACMjI1zUqHPPAZPGrMOhjIsmBI1hA8tyOL81zJuWjilB/P5QkqGsjaEJDE2nKQLvWlXH6rogP3ttAENXZeKM7eI4ShIt7FNZ7GmNYW7Y2DBl0PPIEHMpa04Hn67xd2c1sa07ha4JTo9F0LXZv543V1XqBTfd8ZQSOzz1jPF09B/9KMTrr6vpSE0T5HIumUyBYNBE18s99qRU834eSjUbGxoaqKqqWvQsZnBwkKGhoRl7VQsFIcSoSe/kM4bxeJxMJoMQAsMwin1Yb6ziWMp4je8t1tXVTds+8eyDYrEYTU1NJ4WljzJOBr5x0HW92KuYrI/nZXde4Cu9mE1d45uXnsI3L1X/Ljgu6bxFJj7IG70j1EcbWdNaz554nof2xMk5ksawyb9d3M6LfRnqQiYXlrAdp4LlqAVxRU0AQwjStsPGWJh43sbQLUbyiqCzui7Ar/cmCI4SOnYOZgkagk0VNpsCKWqb68maEWqS/aT1PEFXmZ8KJDV6gc4DB+ns6qE+EuSctiqyBbus7KlrGn5DEDTVWIX3u4CuU8BlUyxEZzxPa6Wfv15fT2vlxKB3JGXNmWDqGue2zk2ot5ScUVFRQUdHR3H4PR6HUGj2pJHxg8rDwz6M0fOkqOySQ4f2AxK/P8CGDQ08/3wI09RpaIDLL1fXmFdxCAaDZeSV+SCdVrOAR5JMeFmwYRiLPojuXYvDw8MALF++HJ/PV+azV+qmMH7G8GgEGK9cORsGqUdM0nX9T9o+6I477ljsQ5gTTujAN34H5pVSDh8+TGVl5aR9PK+0OdPuVkpJaiTOwMAAlZWVnL9+RXHBWlEbZEmln1TBoSpgYGiCNfWzv+DX1AX57YEkuhAsqfazvj7IJR01+HXBHS8NUOnXqQ7onNEUoTdtF4/Hyud5tXOYtatqigtW3nYJjQYuoQk0YdBYFcABXrUFARd29hQ4kBzg3Cik4hlcoWOYBvURP8sr1We6pKOarqRFT9pibV0QQxNU+XU2rIzw3jVRTH3iArMQZc2FhGfT4zhOGTljZAS++lWDQ4cE4TC8//02Z501d4XxNWskQ0OQSgl8PsGZZxqsXLkCy7LI5XLccEOCP/whwfCwxcaNWUZG/AwMWNi2Xaw4OA7cdZfGyIjg9NPdCfZFU0FK+MpXDH7/eyWTdvnlDn/913Mb3SnNOmeTBR9tlM7Bjc86p/LZGz9jWFqK9oLhkXwmT/S+urp62l6n4zj09vYyMjJy0j5oEXBCuzMAxQFbz7jRcRyqq6uJxWLFx5T28WZzU6RSqWKtvrGx8ags6K8OZDiYyBMNmpzeNCY10pMs8EJvmmjIYEtThNv+2MNI1iKVSmG7Dhd0NHDpqY1lr7V/JMcj+0Z4riuFJWF5jZ+Co0xiPViu5HNbW0gXLJ7oHMYtFFhXJXEK+eJYgN/vxzH81ERC+M2p91S2bdPX10c6nb2ZrO8AACAASURBVJ60rNmbKvBMVwpTE1y4rArfJEFzIeG6ajwhHo9PWrb79rd1/vhHvViKDAQk//Iv1qzm40rx4IMad92lDHMNQ3LddfYEw1rveAYHBxkcHCxeO17Gcvvtzbz2WhDT1DEMjU98wuaMM2a+TR96SOO228yiNqiUkltvtVi1ana3uOf6HQwGixq3iwmvtyiEoKmpaV6zbaWlaG+es1AozGvGsFTzs7m5edr+nHcuvRm++bjdn8SscNKdYSp4N1AulyMWi5HJZIq9gMn6eDO9Vl9fH/l8nsbGxqKu3tGAN54wHrEKHxdXqEXAcRzeUufyq10jRCNhVsWqueSUmgnPWVoV4PqNAdjYqHqEwE93DrJraGzezhACTQiqg34uXz22KSjtseRyOXIjQ+zr7SpTRCmVrpqprNmXLvD17T3YrtpovDqQ5ZNnNs2pPzcXeGXEQCAwZdkumy3vv2WzavRgrmv/JZe4LFki6ewUo64QE4OOdz1KKWlvby+WyVR2k+f11wOASy5n4bou992Xp60tM+MiPTgIpS0uKQVdXbBq1fTH7GUmHsswMpWe2zFCaW/xSEWupxKQnuuMoVeurKysnFbz03NQ91ooJ+2DFg8ndOCTUtLV1UU4HKatrQ1N0ygUCjiOg+u6k/bxJoPjOAwMKLJINBo9LqSQPPp0TUUFn3rL6lnv0LXRz/rWpZXsG8mTs1xc4ML2qkmDT6mO41SKKENDQ1iWstfVdZ1oNDplmezZrhS2K4uvfTiZZ89wjpXRhWW4WZZFb29vccMz3YK+YYPLK68oQorrwsqVM3vnTYW1ayc6l0O5a8FkWacQgsrKAOGwryhm7boQjZqYplW2SE9G9z/nHMn990tyOfWa0ejEQfhSSClJJpPFXud0rt/HCh6jVdf1o9ZbnMuMoadQ4zjOjBqkJ+2Dji+cLHWOljphLGB0d3cTCoWKu8FgMDhpICsNMBUVFdTX1y96CSiTydDb24sQglgsdkSDr1nLYd9InrqgSUN4fouMV9ZMpVLF3bm3iABl5zgQCPBIZ5JHO+PFBSTvuNx0VvOkxJjJ0J0s8B87+hnJ2Syt8nPdaQ1l/cXSvlBNTQ3RaHRWm5Qnn9R45RVBVZXkXe9y522+Ohk82Tu/309jY+O0C/ovf6nxox8ZFArQ0iL5h3+wiEbHfu8N23sZeOl57uyM8MQTVfj9Bldf7dLSMvl7lA7GNzU1LbhVz1whpRz1NRw+LhitQHGu0Ofzoes6uZyqjpRm3pZlEY1Gi/ZBNTU1LFmyZNHXiBMIU14kJ3zgsyyrmN15bE3HcYoUdG8B8Zrg3h9PVUTXdRobGxddWcGyLPr7+0mn0zQ0NBwXxAOvrFlVVUVdXV1ZxlA6k1XaY9mdktx9wEbTdAI+k/OWVHP12ukH3ktxyxOH6M2o7NKVknOaI3xgQwMwRqbRdZ1YLDah9yql5PGDCQYyNhsaQqyYQmFmoVDa6/QyhtlgcFD5C7a3z+x+Pn72zftb1/WyrDAQUPOaxUrBHDYFRxO5XI6uri4Mw6CpqWlRGaQwVvrNZDJlLu3jz/OOHTv4/Oc/j9/vp6Ojg82bN3PmmWeycePGRS8Xn0A4Gfgmg5SyGLx8Pt+UOzGvdOfV/dPpNFIq/7FIJFIMhouxkyvNYDxVkcUuSc0UYKbCz3YO8HhnHNu2yBZs3t1usjxMmWTVdBqOUkr+5687y1Qyl1f7+cTpjcWsc7pNwfdf6uOpgyl0Td0xN2xqYHNs4Rep0pGJyspK6uvrj/mMWSndP5vNks/nAVXqq66uprKy8ogZjkeCUsLR8ZLleaSUcDhMQ0PDlN+Z14f0NjUjIyPs3LmTHTt2sH79ej796U8f4yNXcByHz33uc+zbtw9d1/nCF76wqK4OxwAnyS2TwQsamUwGUBRob4ENhUJFooDXx0omk2SzWerq6qisrCwqdXgKEt4uurR0dzRv1lL26PEwDjATW3M6ZCyHJw+m8PtM/D6TcAiGfCEuXhktLtBef0VKWRYIPcUOIQSxiElXajTjc11qNJs9e/ZQWVk5bZ/KlZI/dKmgB2rH98SB5IIHPk/c3LbtRfOCE0IU6f7eIl0oFKiuri46gB8+fBjLsqZ1qjha8OYETdNc9DlBGCOlJJPJGQk+nrdeoVCgvb29KJd2xRVXHMMjnhyPPfYYAHfeeSfPPvssX/jCF7jtttsW+agWByd04NM0jdWrV+M4DplMhlQqVSQJ2LaNpmn4fD4ef/xx7rrrLv7lX/6l7Eb0+XxlckreLtpTkfAsbkqD4VxEdqdCoVCgt7e3yB5dbDr0+LLmfIgQUqJc4Es2aS7KSHW8hqM391aqk+ltOt7T7uPnnQ4jBZcaCpxfK2htnjnACJSBr1tS41jIKYpSNuJMeo0LjYEBuOceHddV83teb2/8IPr4kQCP1OFtOjynivEl0oUKTKVZ3vEwJwiqetHV1UUoFJr2uvY20f39/UQiEVasWHFc2QcBXHjhhVxwwQUAdHV1UVc3+xbCnxtO6FLnVPAo+k8//TRf/epXkVLy/ve/vzjQPtusznXdsl7hZISOqYgzU72etzDU1tZSW1u76D2Y+ZY1J8N/vtTHH3szmJpAE/CxzTGWVs/8eqWbDm8D4zgOhmEQiUQIhUKzylYe3BPn568PIiVEfBqfOKN5Vu8/E0oH9WOx2DFdEFMp+NSnfPT3q5GMigrJF7+YQ4j5yZ9N1i/05NpK74m5bnwymQzd3d34/X5isdiiE0C8Yf1S1/ipYFkWXV1d5HI5Wlpajkv7oFJ85jOf4ZFHHuHrX/8655133mIfztHEyR7fXCGl5Prrr+fKK6/ksssuA9QO2csK0+k0hUJhwk3v9aCmes1SQsdUxJnxC3QptTwUCtHQ0LDo5Z/SsuZCkWmklDzblWIkb7OpIUxDZG4BwjtHwWCQurq6IknJW6THZyuT9WUPJ/IcShVYGw0S8R/Z4uvpfaZSqQXRH50PHn5YDa57+yPHsbnwwh7e+c7Cggyie5tE7xx7/cJSp4rp5MFKA4yX5S02vNED5bc49TnyWN19fX0Eg0GWLVu26O2G2aK/v593v/vd3H///YvO2j2KONnjmyuEENx+++1l/zdZyc0LgqlUing8juu6GIZRXGC9bMPz+ptMZHeygdnS3lUikcB1XVpaWhb9Il2IsuZUEEJwdsvcy7bj6fel39F4X73xyv66rpct0E2RAC2zHJ2YCqUblUgksqgzcNXVclR1SPWfCgWHlpZKWloWZoEuneOcyamitIceCARwHKeoBrNs2bLjIsvzKiqeRNxU8K65dDpNc3MzjY2Nx3WWB3DvvffS29vLhz/8YYLBIEKIRSfCLRZOZnwLCC+IpVIpUqkUmUymON/jKT9484HTuUvbtk06nWZoaKhYSvKC6bEizkyG0rLm8TDCMd+ZvNLnFwqFCdnKTBn4dDj+ZuDgi1+0eeQRgabpnH++zuc+53CsK+TjnSpSqRSu6+Lz+YrMaK9fuBgBxBubME2TpqamaYOwZx/k8/lYtmzZcW8f5CGTyfD3f//3RQ7DDTfcwIUXXrjYh3U0cbLUuVhwHKeYEXrZoeM4EzINz8Hbs+dJp9PFoXjPCLW0RHq0iDOT4WiUNY8UR6tvNn6BzuVy2LY9QQ1lfKm5NAgfD+anMKZOk8/n0bRmfL4Qzc2ebdXiwBsJCIVC1NXVFeXuvHNdytidymh2IVE6HD8TocbT40wmk8RiMZqbmxf9O15oeGvTnwlOBr7jBV6W4QXCVCpVJL3s2bOH22+/nXXr1nHTTTdNexPOhjgzH5LB+GOdbgh9MXAkIxPzxWSCBp4wt1fGjsfj6Lo+b8HkhUSpolB1dTV1dXWLToIq7XdONxJQ2i/0/i7dJHrjFQtxHZZKoM00HJ9MJssYsH+q9kFToVSAP5/Ps3v3blpbW4syhH+iOBn4jmcMDw9zyy23sG3bNj7wgQ9w5pln4jhO2eLqlUina7TPhzgzFbLZbNF1/ngpa3rGnosx9D3+WCzLIpPJMDQ0RD6fL/a6xhM6jnVGUCgU6O7uxnVdmpqaFv17A4rOJ5FIZNrB78lQKoJeqvAz3ltvLqX/uQhdl9oHNTQ0LLoO70LDsqyygP+73/2Or33ta3R0dLB27VquvfbaRd/sHgFOBr7jGTt27OC3v/0tH/zgB4s7yUKhUEacyWQySCkxTbNsyH46M81S4oz3ZyZm4/FY1szlcvT09CClJBaLHRc9FU+Rv1TFo1SYO5vNYtt2sbd7tMvRpaXWYz0nOBWmkvc6UpR663nBcLZ2Qt6AuaZpNDc3T5vleQFb0zSWLVv2Zyc1Zts23/rWt2hra+PKK68klUpx4403ctNNNxGLxUgmkwQCAVqmEnU9/nEy8P2pQ0pZJAV4gdDLNMYvrjMRZ0rLSF4pKRgMIqUknU5TVVW1qBmVh1Lj0yO1oFkolLo6zLSYl6r6L3Q5Op2Gf/93neFhwbJlBd70pv3ounZclFphbGNQ2qc+mii1EyodXynNCD02b319/QT3i/Gv5dkH1dXV0drauuj3wtHC9u3bueWWW1i6dCnXXnstDz74IIODg5imiWVZPP/887z3ve/lxhtvXOxDnQ9OjjP8qUMIUbRLaWhQosse+9PrF/b09OC67qTSad6NaxgGFRUVZYoziUSiKAVmGAbxeJxcLndMMpWp4C2cnmLGYlPdS/ud1dXV0/quedB1vWwEZnw5ur+/vzjzNtey3a23GuzYoWHbBZ5+2sGymrj++sXT1vTgkbOy2SzNzc3HrBc2mZ2Q51ThZW7evZFOp4u2Qh6pzIM3SC+l5JRTTvlT73FNgJfoeNfJyMgI+/btIxqNcuaZZxIIBHj88cc5/fTTOeWUU3jttdf43e9+N2sT7j8VnMz4/ozglYC8YJhKpcrGKcbrkA4ODtLd3U0gECgrax4L4sxUKB0HiMVixwWJwCu1AjQ1NS3okPJUZbvphLmlhKuv1kgkCmiaht/vZ906uOUWa8GOaz4YL7y92L2w0s1KNBqlpqZmgqhBLpfjueee45lnnuGUU06hra2NjRs3snLlykXfbC00SoPXrl27MAyDlpYWOjs7+fa3///2zjwsqrL94x9mGBYBRUHZUVBBEBW33FJS85I0FXNDXsWtcrdcc+lSM1IzfW1xRd8sDXOtrjTN3M0MUlPRlDH9iQsi+z7AwMz8/qBzGnaRbcTzua6uZM6ZmeccmLmf+3m+9/cOxcfHh/Hjx5OUlMTNmzdRqVRs27aN4OBgg/AafQakpc4XlaI+pILjzOnTp9m/fz9jx45lxIgRWFhY1JhwprT3EAQHhmLHpl/QXJNLrcLEQ3+ZVJD5C0t2S5bYkp5uhbGxMTod9OiRz9y5mmofW0kIMv/c3FyDqF2EgglUbGwsGo0GR0fHUicrOp2OmJgYjh49yp07d3j06BH37t2jadOmTJ48mQEDBtTwyP8lLy+PxYsXExMTg1qtZurUqfTt27fSr7thwwZ++eUXfH19SUxMZPbs2Tx8+JAdO3awdu1aHj16xK+//kp4eDgzZsx4nm3NpMAnUUB0dDRz5szB2NiYqVOnYm9vL36xVsSH9FmEM6UhLC8pFIoa97IsjczMTJ48eSLaVtW2RVxeXh4pKSkkJycjl8u5fVvBt9/akZlpipublkWL8rC1LV3oVB3ot1cylLIJffVvefWUQg2fUK7TtGlTFAoFarWaqKgoGjZsiIuLSw1fwb8cPHiQqKgolixZQkpKCkOHDuXMmTMVeg2tVlvod3L27FmOHTvGypUr+fbbbwkNDWX79u3Y2dmxY8cOfvvtNzw8PHj//fdrzUygCpECn0QBN27cIDo6moEDB4p/1EKGURkfUihbOFNSMK2Nmrzy0N+jsre3Nwgln5BR6QtqhHrQrKxscnP/NTXQFzpVZxuh/Px8YmNjUavVODo6GoTSVsjy8vPzcXR0LLOUQ799kKurq0GoYIsi9P20tLQkJSWF4cOHc/Lkyad+vn7Qi42NxcHBgePHj3P58mUePXqEWq1m6dKlXLp0iTZt2uDs7MymTZto164dffr0qa7LqkmkwCdRMYr6kKpUqjJ9SEuiaKsmfccZIyMjsrOzqV+/Pk2aNKn1/RT9TEFQtRpS9vI0GVVpysZnycLLGpMhZnnCmBo2bIitrW2ZWZ5++6BmzZoZxApDWWRmZjJ16lRGjhzJoEGDKvTc6OhoVq5ciVwuJygoCBMTExYuXEhwcDATJkwAYMyYMYwfP74u2pdJgU+iclSVD6mwrCn4NAqdv2tCOFMaubm5oiLWUIq+9RvWOjg4PHNGpb83W5ITir77THkIwqPc3DwiI11Rq03p319Dw4bPNLQqQciGBW/Usu6TUNyfk5ODs7NzmQHSUIiNjWX69OkEBQUxfPjwcs/Xz/IePnzIkiVLmDZtGjY2Nmi1Wjw9PZk/fz4WFhb4+voSGxvLkSNHCA0NxcHBobovp6aRyhkkKofgImNubk7jxo2B4j6kcXFxpfqQZmdnc+XKFezs7AoJRUqS+Fe1cKY0tFotSUlJYr1WWbVdNUVVe36WVL6ib8ydnp5erjF34Wy4IZs2uREeLsfICH74Qc7nn6v5p8KmRklPT+fJkyfllpcUbR/k7e39XLQPSkxMZOLEiSxdupRu3bqVea4Q8GQyGZmZmVhaWmJiYkJmZiYJCQmcOXOGjIwMkpKSGD16NA8ePCAiIgK5XM7XX39No0aNauiqDIMXMuM7fvw4P//8M+vWrSt2bN++fezZs0cUf/Tu3bsWRvh8UpIPqUql4tKlS+zcuZOXX36ZGTNmYGlpWaadV1UKZ0pDyDxNTEywt7evdfEK/OsdKZPVbCF6UWNu/eJvExMTsZzF0dGRBw/MefNNE4TbpdPBiBH5TJ1ac4pSYR9W2PMsS0Uq7PupVCqcnJxo0qRJrU9unpaQkBCOHj2Ku7u7+Ni2bdvKXJE4cOAAe/bswc/Pj8GDB3P8+HEiIiLo1q0bPj4+7Nmzh86dOzN69GjUarXBL/NWEmmpUyAkJITz58/j5eXF+vXrCx1LSEhg4sSJHDx4kNzcXIKCgjh48GBd/+OoNlQqFbNnz+b+/fvMmjULT09PsrKyyMvLq5APKfxbjKz/5VyWcKY0BBstQxLU1FbZRFnk5+eLY1IoFKJ3bGxsfd57zwFTU7mYYdVk4BOMDcqrFRSy1Li4OMzMzHBzczOIJeyqRn9pc+fOnZw5c4ZFixaRmppKx44dxWORkZG0bduWWbNm0b17dwIDA+tcUXoJSEudAh06dODVV19l7969xY5FRkbSvn17sbGmq6srUVFRtG3bthZG+vyj0Wjo27cvAQEBhSYPRX1IU1JSyvUhNTY2xtLSUlRZFhXOpKamFmvVpN/fTV8AUb9+/VptDquPfubp5uZmEJmnvtG10FVcMItu3Dib7t1zuHDBBNBiZ6ehV68kUlJMq9WYW9/3s7yGzMK+X2ZmJvb29jg4ONSpL/isrCyWLVvGlClTcHZ2FgO6iYkJ3bt35/fffyc+Pp5du3ah1WqZNm0ay5cvR6PRMHDgQAIDAwHq1D2pKHU28O3fv5+vv/660GMrV65kwIABRERElPiczMxMcS8ECjquZ2ZmVus46zJWVlaMHDmy2OPCxKLhP6qIoj6kwn5SWT6kwjFTU1Osra2Bwq2a0tLSRLcVU1NT8vIKXE2cnJwMwg1Go9GQkJBARkaGwWSeRZ1O9CX++p3WP/kETp2SkZ4uo0ePXBSKguXQ5OTkajHm1u/u4O7uXqYQR9j3UygUtGrVyiCK6auaixcvcvjwYXFfbt68eeJE/dChQ+Tl5fHSSy/h5+fHqlWrMDU1JTQ0FJlM9sLt5ZVGnQ18I0aMYMSIERV6jqWlJVlZWeLPQjNYieqlqnxIZTJZIW9MweQ6JSVFnBU/fPiwRoQzZaHf2cFQMk+1Ws3jx48BaNq0aZniD5kMXn1V+89P5v/8V4C+Mbf+5ONZVLtP28NPOPfJkyekp6djZ2f3VF6qzyudO3emadOmuLq6cvnyZcaPH8+cOXPo3r073bt3Jy0tDbVaze3bt8XSnPr169f2sA2KOhv4noW2bdvy6aefkpubi1qt5u7du3h4eNT2sF5IjI2NadCggWgSXNSHNCMjg4SEBKBkH9K//voLjUZDw4YNcXd3F5da9YUzWVlZJCYmVrlwpjRqy8C5LPRVpFWhbK0qY279Tu3lTQ702wd5enoahOlAVaK/F6fVajE3N8fPzw+ZTMb69ev56KOPWLhwIW+88QZDhgxh586dXL9+HYVCwXvvvScFvRKQAh+wY8cOXF1d6du3L2PHjiUoKAidTsfs2bOfWfack5PD/PnzSUpKwsLCgo8//rjYMsOUKVNE8YCpqSnbt2+visupkwguMmZmZtjY2ADFfUgTExNJT09n7969XLx4kRUrVuDm5lZo5q9fliGgL5xJSUnh8ePHzyScKQ39/cUGDRqUu1xXU+Tm5hIbG4uRkVG1FXIbGRmhUChQKBTiF3BRY279/VkzMzOx3MLe3r7ML22tVktcXJwoCnJ2djaI+1qV6ItXdDqd+G9BPQ0QFRWFtbU1u3fv5saNG/j7+zNx4kTs7OxqbdyGzgun6qwpduzYQWZmJjNnzuSnn37iypUrvP/++4XOGTBgAD/99FOt7+3UFcLDw5k/fz5du3YlODgYY2PjZ/YhLc1xpiThTFno22hVphC9KqlIB/KaQqPRkJaWRmJiYqH6waL3XMjEVSqVuDTbrFmzOpfVFG0ftHr1aurXr4+Pjw+9evXi7NmzTJ48GXt7ewYPHsycOXOIjo7mzJkzdO3alVatWtXm8A0FqZyhppkxYwZvvvkmvr6+ZGRkEBgYyE8//SQeT0xMJCAggNatW5Oens7bb78t1QxWkvPnz2NmZkanTp3Ex6rKh7SirZr0hSJVUYheVQi1gnK5HAcHB4NQkeo3HNbP8vLy8gqVsNy6dYsNGzbg7u6Om5sbHTp0wM/Pr04KWAQePnxIWFgYCQkJeHp6smfPHnbu3EmjRo2YOHEigYGBBAQE1PYwDRWpnKE6KUlBamNjIwpjLCwsyMjIKHQ8Ly+PiRMnEhwcTFpaGqNHj6Zt27biMp5ExSmpfUpRwQsU9yFNTU0t14e06OuU5ThjYmJCTk4OMpkMV1dXg6gfEzoRpKSk0KRJExo0aGAQgTg7O5vHjx9jamparOGwsEQqfI5sbW0xMTHh77//Jj4+ntDQUBYtWoS/vz+rV6+urUsoxLVr11i7di27du2q8HOvX79Obm4upqamtGzZkuzsbGbMmEGrVq1Es43k5GQ+/PBDtm7dSn5+vrg8LTgmSTwdUuCrAkpSkM6YMUNUiGZlZRVbirG1tSUwMBBjY2NsbGzw8vISOyFLVC8KhQJra2uxDKKoD2lR4UxJPqQl7V1ptVpRWWhqaopGo+H+/fvFMsuaNuTOycnh8ePHGBsbG0ytoE6nIyEhgdTUVOzs7MQmyKWdK7QP8vLywt/fX7yHQjZvCGzbto0ff/zxmZaz16xZQ3h4OB06dODUqVP4+PiwaNEixo8fz8GDB7l79y7Nmzdn+vTpTJw4kd27d/P6669z7949ACnoVRAp8FUTHTp04OzZs7Rt25Zz587RsWPHQscvXLhAWFgYoaGhZGVl8ffffxeyJpKoOSrrQ1rQH+82ULD02bx5czG4VLdwpiz0HWEMKcsTArFCoSg3EOfm5hITE0N+fj7NmjUrNjEsKlSqTVxdXfniiy9YsGDBU52v0+nQarWEhISQkpJCWFgY5ubmjB49mn379vHWW29x+PBhzp49y/Hjx2nSpAlWVlbMnDmTTz/9lG3btkkT5WdE2uOrJrKzs3nvvfdISEhAoVCwbt06GjduzJo1a/D396dt27Z89NFHXLt2DZlMxptvvlnhtiBarZbly5ejVCoxMTEhJCSEpk2biscl39GqoyQfUqHjwf79+/n999/59NNP8fDwqDHhTFlkZ2cXau5rKFmeIKopLxAL5yYmJmJlZUWzZs0M4hrK49GjR8yZM4d9+/Y91flqtZp58+Yxe/Zs3NzcxMdVKhWjR48mICCAPn36sHTpUsaPH0/Pnj0xNjYmPz+/1lt5PQdIe3w1jbm5OZ9//nmxx/Vng0uWLKnUe5w4cQK1Ws3evXu5evUqq1evZvPmzUCB7+iuXbsK+Y726NFD8h19RvSdYoRZdkREBEuWLKFt27Zs2rQJhUJBdHR0mT6kFXGceZaib/0sr7wlxJpEaPwql8vLzfKEYvrc3FxcXV0NRhhUHTx8+JBr166JLYGE8oV69eoxZswYrl69yoQJE2jfvj0///wzL730EsbGxlLQqyTS3XuOuXz5Mj179gTA19eXGzduiMck39Hq58GDB3z44Yfi7wCezYe0IsKZshxnBN/PkoQitYV+gXx5pROCEjY+Ph4LC4vnpn1QRdCvywNwc3OjRYsWhIWFMWnSJGQymdg1IS4ujqSkJADeffddsrOzDWZZ93mn9j8ZEs+M0HdLQC6Xi0sgku9o9VOSJV5V+ZCWVPRdkuOMftG3SqXCwcHBYGrahMztaQrkn+f2QU+LftD7+eefycvLo2/fvnTr1o1r165x6dIlOnXqJN6nrKwsBg4cCBT8/qWgV3VIge85pqi3qCDJL+mY5DtaO1SVD2lpjjNFi77j4+PJyMioduFMWejXMJZng1a0fZC3t7dBlH88K87OzsX294o2iV2zZg137twhIyODO3fu4OfnR0ZGBgsXLmTMmDE0b96cHTt2YGZmxpQpU4AXu5NCdSAFvueYDh06cPr0aQYMGMDVq1cL+YpKvqOGS2V9SI2MjMjNzSUhIUH0/bSysiq3VZPw/8oKZ8pCaGmk0+nKNbvOz88nNjaWrKwsHBwcsLe3r3Nf8Pr1dQ8ePODAgQOkpqaye/duYmJiWLJkCd7e3kydOhV7e3vuYW44/gAAEolJREFU379PVFQUvXv3ZuzYsbU8+rqLpOp8jhFUnbdv30an07Fy5UrOnTsn+o7u27ePvXv3otPpmDx5Mv3796/0e5WmIA0JCeHPP/8U96k2bdokZZiVoKgPaVZWFvn5+chkMpRKJRs2bGDSpEkEBASUGVwq6jjzrOh0OlJTU0lISCjW0qgkhCxPoVDg7u5ep5fxsrKyWLNmDWZmZlhZWXHs2DG2b9+OnZ0d3377LefOnWPOnDm0bNkS4EXojF5TSJZlEpXjl19+4dSpU6xevZqrV6+ydetWUUEKMHr0aDZu3Cj1+6omhMCyatUqLly4wNtvv42Pj88z+ZDqC2eEkozKtGoS9uc0Gg2Ojo5lBuIXoX2QfjeF5ORkJk6cSPv27Vm2bBn37t0jLCyMBg0aMHPmTABmzZqFnZ0d7777rkF07KhDSOUMLxJarVYUSVQVZSlItVot9+/fZ+nSpSQmJjJ8+HCGDx9eZe8tUbDHc+XKFSwsLDh69ChWVlbFfEhTUlKIi4sr04e0osKZshxnhP25+Ph4GjZsiK2tbZl/c0IfQrlcTqtWrersl7yRkREajYaLFy/SsmVLnJ2duX//PlCwB9i1a1dOnDjB0aNHee2113j//fdRq9V19n4YIlLgq4Poz6AFdwgjI6NKzazLUpCqVCrGjBnDhAkT0Gg0BAcH4+PjIznEVzF9+vShT58+4s9V5UP6LK2aTExMxI7r5fmRCg1lX5T2QQCnTp3iwIEDBAcHs2TJEsaPH8/hw4d5/fXX8fX15f/+7/84fvw4PXr0EEVPEjWHFPjqGJGRkTx48AA3Nze8vLyQyWTF9m9SU1O5ePEir7zyylO7YZSlIDU3Nyc4OFj84uzatStRUVFS4KsFqsKHFAoEOJaWluJkR184k5aWhkqlAsDMzIy0tDRyc3NLFM4IDWUBPDw86ty+7507d8Qu5xqNhiNHjjBo0CD69evHjRs3OHfuHE5OTsydO5fPPvuMnj17YmtrS9++fRk8eLDBlJ68aEiBrw6h0+k4dOgQx44dw9vbGysrK6ysrFCr1WK2oNPpUCqV/Pjjj/Tr1098XnnLomUpSKOjo5k9ezbff/89Wq2WP//8k6FDh1brtUo8HVXhQyq8jlwuJzMzU/TNNDU1FfcJMzIyiIuLA+D06dPk5ubSsmVL7OzscHZ2xsXFpc4ZKet0Oi5cuMDdu3d5++23SUlJ4ZtvvkGj0RAQEMDw4cPZvHkzZ86cYfjw4Zw4cYL58+cTGhpK8+bNa3v4LzRS4KtD5OTkEB0dzYIFC/Dy8uKbb76hT58+aLVajh49SrNmzUhOTmbu3Lk0b96czMxMseC66FINFHxBymQyjIyM6NevH7/99huBgYGiglS/c/2gQYMYOXIkCoWCIUOGiAo1CcNDLpdTv379Qnt8RX1IExMTAUTRS3h4OAcPHmTdunWFBCklOc7ExMRw8uRJfvvtNx48eICrqyvt2rVj4sSJBmHEXp5CuTyEiaKRkRHdu3dn27Zt3Lp1i88++4zAwEC+//57OnbsiIuLC76+vnz55Zc4ODiwYMEC4uPjq/HKJJ4WKfDVIZKTk8nNzaVz585s3bqV06dPExsby8CBA7l79y5Hjx5l1KhRODs7Y29vT2RkJGfPnsXd3Z1Ro0YRFxeHXC7H1tYWKNzqRCaTsXz5cgDxQ68/a33rrbd46623qvR6SuttdurUKTZu3IixsTHDhg1j5MiRVfq+Lxol+ZBqtVrRAm3t2rXcvHmTyZMnk5qaSm5ubok+pAApKSnY29szY8YMXF1d0Wq1KJVKIiMja+vyilGWx215FO17p9VqcXZ2BgqW/AcNGiQGwbVr1+Lq6oqDgwPm5ubY2tqKny2J2kUKfHUIQYBgZ2dHVFQUH330ETKZjMuXL9O0aVOcnJxIS0ujQYMGzJs3D3Nzc7788kteeeUVAA4cOEBcXBzTp0/n3LlzJCUl0a5dO3x9fTE3Ny+WEQozX41GQ2JiInZ2dlV2LaX1NsvLy2PVqlUcOHBAbOHSu3dvcRlPomqQyWRYWlqyYsUKPDw8+O9//4uxsXGpPqQmJiao1Wq0Wi1ubm6FylratGlDmzZtavFqClOWQrksdDqdGPRCQ0OxsLCgU6dOfPXVV3z55Zd8+OGHrFu3juDgYGbNmkVgYCAqlYply5YVa0smUbtIga+OoNPpuHnzphicvL29uXbtGtOmTaNbt25cvnyZ9u3b88cff6BSqahfvz5KpZKcnBy8vb0BSE9Pp2nTptSrV4/PPvsMX19f7t27h1KpJC8vD5VKRcOGDfHz88PZ2Rm5XE5kZCQnT54kPDxcfH5QUBC9evWq1PWU1tvs7t27uLq6iq4nHTt25NKlS7z22muVej+Jkvnqq68KZTil+ZCmpqaiUCho0aKFwbcPKkuhXBJClmdkZERcXByrV6/GysqKdu3aMW7cOD7++GN69+5NZGQkP/zwAwMHDmTXrl1cuHABPz8/qRjdAJECXx1B2Idr3bo1AIMGDWLVqlVERUUhk8l48uQJO3fu5N69ezg7O2Nqasq9e/ewtLSkQYMGZGZmkp6eTtu2bUWrqw0bNgAFxeshISFMmzaNqKgojh07xpYtWzAyMmLZsmV07tyZ7du3Y2pqyrlz59i3bx+Ojo60aNECnU5XSDzztLWF/fv359GjR8Uel8y3a5ayBCkl+ZA+D5SlUC4J4R4cOHCA8PBwvLy8CAoKYu3atXTs2JF27dpRv359Ro4cycqVK9m/fz8bN24UxWMShocU+OoQjo6OODo6otFoaNeuHRs3buTChQsYGRnRq1cvTExMSE9PJzIyUlThCbPzv//+m7i4OBwdHcWsCgqKjh8/fkznzp0JDAwEoHfv3iQkJPD7779Tr149Fi9eLI7h1Vdf5bvvvuP48eM0b968WCG9EATv3r1LXFwcLVu2rNAypWS+LVFZylIol4RKpWLVqlU8efKEV155hdOnT3PkyBH8/f1Zvnw5ixYtokuXLgQEBGBjY4O7u3uhjFLC8JACXx1CCCrCDNXGxoZBgwYVOicgIABra2vS09Pp2rUrP/zwA3PnziUjIwONRoOTkxPnzp3Dx8cHKAh88fHx4nLopUuX8Pb2Jisri+joaNq3b1/ovQGGDRvGli1bmDp1Ku+99x6vvvoq+fn5tG7dWgyoJ06cYO/evVhYWGBubo6LiwstW7YkMDBQrEEriebNm3P//n1SU1OpV68ely5dYtKkSVV7IyXqNCUplMsiKyuLx48fY2try3/+8x/CwsKYMGGC2JZKsF8DpH6XzwlS4KtDlLSMWNS+zM7OjlGjRgHg5OTEt99+y40bN0hLS0Oj0WBsbEx4eDhDhgwBELt5d+7cGYBbt25hY2ND48aNycjIEIOU8FwocKE3MTEhMzOTK1eu0KhRI1JSUtiyZYuodMvOzmbmzJkMHTqUJ0+ecOXKFf7880+ysrKwtrYWHWcEDh06hEqlYtSoUSxcuJBJkyah0+kYNmxYlYlqSlOR7tixgwMHDoiCjQ8++MAgZPkSz4ZMJmPFihVPfX7jxo155513WLFiBdevX2fOnDns3r2b8PBwEhMTUSgUtGvXrhpHLFHVSIGvjlOSEhP+DZINGjSgR48ehc755JNPxC/5nJwcVCqVmKldvXqV5s2bY29vj1wuF+uShPfJyMggIiKC3r17c/36dbFUolmzZqxatYpDhw4xZcoUYmJiyM/PB8De3p7XXnutkEDFyMgIV1dXsbeZkLlqtdpi1l1VQWkqUoC//vqLjz/+WMyCJV48WrduzRtvvMEXX3zBF198gZeXFxcvXsTMzAx/f//aHp5EBalbhnkS5VKSeXXR7MrZ2Zl69eoB8NJLL7FmzRqxZs/BwUEU0IwbN46UlBTOnj0rBr5ly5Yhk8kYMmQIf/zxBz4+PmJGFhMTg4uLC/Hx8SQnJ3P+/HlGjBhBUFAQixcv5tatW0BBoFm4cCFr167l8uXLoj0WFPchhYIWN4IJ8LMiqEhL4q+//iI0NJTRo0ezdevWSr2PxPOJXC5nwIAB2NjYsHz5cpycnAgICJCC3nOKlPFJlNvJQV+OPW/ePPHfHh4eDBgwgM2bN/PJJ59gY2ODs7Mz06dPp3Hjxty6dYshQ4ZgamqKWq0mOjqaOXPmcPv2bVq0aMHnn3+OhYUFt2/f5sqVK1hbW3P58mV27NiBl5cXjRs3ZsWKFXTp0oXg4GAuXbqEq6srHTp0EMcNBUurJ06cYPbs2Zw9e5aGDRtWeK+lNBUpwMCBAwkKCsLS0pIZM2Zw+vRpevfuXaHXl3j+sba2ZsyYMcTExNT2UCQqiRT4JCpEUWszf39//P390Wg03L9/n6ZNmyKXy0lNTRWL2mUyGY8ePUKlUuHu7s7JkyextrYWra48PDxEZd369etp3749wcHBKBQKvvvuO+rVq4dMJuPw4cMMHjyYDh06sG/fPpKTkxk9ejTW1tbMnj2bmzdvcunSJbp161Zl16vT6Rg3bpyoHPXz8+PmzZtS4HtBad26tbjiIfH8Ii11SlSIkvw8tVotcrkcd3d35HI5Op0Oa2trvvrqK/FLIjY2FhcXFwBu3rzJjRs3RNf+oq/n4eEhLmM6OjrSrl070tPT0Wg0ogPGgwcPyMnJQavV8uabb3Lo0CF2797Nrl27SExM5M6dO6jVaoBCy7gVJTMzk9dff52srCx0Oh0RERHSXp+ExHOOFPgkKoVcLi8WDIUlSEtLS7Ebd48ePfj666+BAqGKmZkZCxYs4I033mDSpEl88803JCcn06JFC/744w9MTEyIjY0lLi6OJk2a8OTJE4yNjXFyckKr1ZKUlISLi4sokOnXrx/NmjWjS5cuNGnShFmzZnH69GmgIFifPn26UP1feRw6dIi9e/diZWXF7NmzCQ4OJigoiBYtWuDn51epe5aXl8f8+fMJCgpi+PDhnDx5stDxU6dOMWzYMEaNGiWKeyQkJKoOaalTokbQXyLVV2UKJQ+pqalYWlrSp08fVqxYQUhICPHx8WRlZeHk5ERERIS41yhkew4ODty/fx8zMzPMzMxIT0/n5ZdfpmvXrowfP56oqCj69+/P7du3Wbp0KQcPHiyzy7Wzs3MxFSkU1D4GBARU2b348ccfsba25pNPPiElJYWhQ4fSt29fQPIilZCoCaSMT6JG0M8KNRoNGo0GnU6HpaUlPXv2ZNCgQZiYmODp6cnq1avp1asXarWal19+GWtra2xsbEhMTOTq1avs37+fmJgYXF1duXHjBt7e3uTk5IhF7VBQvB8ZGUleXh7/+9//mDt3rsHYavn7+/POO++IP+vbgul7kZqYmIhepBISElWHFPgkahy5XC6a/kLxPTgXFxd69eqFkZERDg4OaDQa+vTpw4ABA9i1axdJSUl06tQJMzMzfv31V3x8fMT9QmFPMTc3l2bNmhEWFoZara7SjK2yWFhYYGlpSWZmJrNmzeLdd98Vj0lepKVz/Phx5s6dW9vDkKgDSEudErVO0T1CQLQ48/b2Ri6XY2Vlxbhx48TjOTk5yOVyunbtSvfu3bGxseHatWs4ODjQsmVLfH19CQsLIzo6msmTJwPFFam1SWxsLNOnTycoKKjQsqrkRVoyISEhnD9/Hi8vr9oeikQdwEhQz5VCmQclJGoKocjeyMio1OB1+/Zt0tPT6dSpEwBdunThgw8+oH///k/dFaImSExMZOzYsSxdurRY6UVeXh4DBw5k37591KtXj8DAQDZv3lylvQ6fR44cOUKjRo3Yu3cv69evr+3hSDwflPqhlzI+iecCffNtffQ7Ygu1gBEREdy5c4c2bdoYpLPGli1bSE9PZ9OmTWzatAmAESNGkJ2dXW1epHl5eSxevJiYmBjUajVTp04VBTVgOH6k+/fvF9W/AitXrmTAgAFERETU+Hgk6iZSxidRp0hKSmLdunXk5OSwdOlS0fDakDK+2uDgwYNERUWxZMkSUUl65swZ8fi8efMYP368QdcoRkREsGfPHinjk3haSv3Qlxf4JCQk6gCenp6WgJFSqczw9PS0AS4qlUp3veO3gL8Ae+AnpVK5qpaGWiqenp6vAFOUSmVgbY9F4vnGMHb6JSQkqhWlUpn5T9CzAg4A7xc5ZQ8wBegDvOzp6fl6TY9RQqKmkDI+CYkXBE9PTxfge2CTUqn8Uu9xI6C+UqlM++fnaYCNUqn8sHZGKiFRvUjiFgmJFwBPT0874BdghlKpPFnkcH3ghqenpxeQRUHW9yUSEnUUKeOTkHgB8PT0/AwYBUTpPbwNsFAqlaGenp5jgVlALnBSqVQuq4VhSkjUCFLgk5CQkJB4oZDELRISEhISLxT/D+h6M9DSPJVwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = [2,3]\n",
    "\n",
    "for cluster in clusters:\n",
    "    \n",
    "    print('-'*100)\n",
    "    \n",
    "    kmeans = KMeans(n_clusters= cluster, random_state=0).fit(X_train_robust)\n",
    "    \n",
    "    # define the cluster centers\n",
    "    cluster_centers = kmeans.cluster_centers_\n",
    "    C1 = cluster_centers[:, 0]\n",
    "    C2 = cluster_centers[:, 1]\n",
    "    C3 = cluster_centers[:, 2]\n",
    "\n",
    "\n",
    "    # create a new plot\n",
    "    fig = plt.figure()\n",
    "    ax = Axes3D(fig)\n",
    "\n",
    "    # take the scaled data in this example.\n",
    "    x = X_train_robust[:,0]\n",
    "    y = X_train_robust[:,1]\n",
    "    z = X_train_robust[:,2]\n",
    "\n",
    "    # define the axes labels\n",
    "    column_names = indicators_df.columns\n",
    "    ax.set_xlabel(column_names[0])\n",
    "    ax.set_ylabel(column_names[1])\n",
    "    ax.set_zlabel(column_names[2])\n",
    "\n",
    "    # create a new plot\n",
    "    ax.scatter(x, y, z, c = kmeans.labels_.astype(float), cmap='winter')\n",
    "    ax.scatter(C1, C2, C3, marker=\"x\", color='r')\n",
    "\n",
    "    plt.title('Visualization of clustered data with {} clusters'.format(cluster), fontweight='bold')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How should we interpret these results? Personally, when I saw the three cluster one, it made sense. We have some companies who perform horribly across all three metrics, that would be the dark blue region in the lower left-hand corner. We then have our average company, denoted by the color green; these are companies who might beat on some metrics, lose on some, or do average. Finally, we have our \"show stoppers\" these are companies denoted in light blue and represent the companies who are crushing every metric.\n",
    "\n",
    "Again, there might be different ways to interpret these results, but for me it visually made sense, and while they might not have a nice spherical structure there is some type of grouping there albeit weak in some instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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jMJwhXbB2w7SgCtei8nk0BoMhFPLbpv8E8ER+XtNw9hnClSRylGls5BTpADSnIh9xNWE+0TtHSWYi6ylPcbpTn/ACSg3JzITw8AK5tMFQqDAZuYaQiSKc8dzCX+xkPolUIZbbaUKEj0B/lfl8zkp2cgIHcBGVeYcudKR2vozT6YTRoyvxwAOwdy/UqAF33AGPP54vlzcYCiVG6BtyTWuq0Zpqlvu+ZzVvsJBUMgFwAivZw8P8ykoepISNX+Bs8sILMHJkFdxRyXv2wMqVkJ4OTz+d55c3GAolpgyDIU/4if9OC3xPNnCIL1iZ59c/eRLGjwen09vclJ4O334r5h6DoShihL4hTzhMsu2+/STl+fXXrYPEROt9mzfDoUN5PgSDoVBizDuGPKE+8fzJdr/t4ThoRZWAr01Kgi+/hKNHoVs3aGUR9p+VBVOmwMaNcOml0M4n8rRmTYiPh8OH/V8bGwulS4dyNwbD+YPR9A15wmO0poZFZm4nanM9DW1fN3UqNG8uztbBg+GKK+Cuu7zNMRs2wGWXwU03wXPPQefO0LMnnDqVfUyFCrLdiuPHYfnyXN6YwXCOY4S+IU9oTiXGchM30JDalOECyvMILfmZXrZF2U6dEgfrpk3e2779Ft56K3tbv36weLFo+wDJyTB5Mjz1lPf5PvwQYmL8jfcnT3qfz2AoShjzjiFXpJDBaFaylSPUJZ77uIhon49TO2owMYROXF9/LeYaK37/XaJxVq2CRYusj5k7VyaCMJcqs3QpJCdbB+cnJJjYfUPRxAh9Q8j8x376MJF/2Hd62+esZCw3obCvzpkTR4/a70ty+X537ICUFOtjjh2T6Bx3bb7SpSEszElWlv/KIjoaHKYKtKEIYsw7hpB5llleAh/gb/byDDPP6LzXXAMlS1rva9pUfnfoIElWVjRqlC3wAS6/HBo1so4Uat8+e0VgMBQlzMfeEBJ7OclCi6gcgIXs4CCnLPcFQ7Nm0KuX//a6dbOTqUqVEsduhM8atUwZePRR721hYdC//y7q1fPe3q4dvP12rodpMJzTGPOOISROkU4yGZb7Usgg2VWLJ7eMGgUNG8L06eJwbdxYBH7jxtnHvPoqVK4MEybAgQNQuzY88ABce63/+Vq1OklCAowYAfv2yXnuvtt/0jAYigrmo28IidqU4SIqsZzdfvsuohLVzrCBSlgYPPOM/NjhcEgET79+wZ0zNhYGDsz9mDZtgtGjJZLIHSpq/AGGcxVj3jGEhAMHT9KWeIp5bS9HDE/SplD0yF2yBMaMgd3+81LIjBwJbdrA0KESAtqrF1x/PaSlnfm5DYaCwGj6hpC5nSZUpSSj+ZtdnKAqpXiAFrT3Cc904mQbx3AANSid5xPCpk3w0EOwcKEI5fLl4fLLq/Pjj7lz2u7fL6Ykz5INWVmSQPb66/DKK2dv7AZDfmE0fYMthzjFA0yhIR9zXZM53MyPLGcXi9jOl/zDHk5SnVgeoqWfwJ/FZq7gaxryMYqPuYIxzGWL1zFTp8KVV0LVqlC/Prz4ImRYuwtyxOkUu/7cudla+IED8PPP5Xnttdyd86uvpDKnFQsWwLhx4hBOSMjd+Q2GgiCgpq+Uag5UB/7QWh/z2N5Daz0trwdnKDgyyOIGxmfXz4mGn1nHn2wjnSyOkB0sP42NjOAabuYCABI5wv1MYTvHTx/zB9u4jyks5D6qEcsnn8CTT0pcvZs334SZM0VTL+ZtPcqRP/6QLF1/HEydKiUdQiU11X7fihVw++3yd4kS4kQeMwai8r5itMFwRthq+kqpJ4DvgQcBrZS60mP3q3k9MEPB8h3/WhZM288pL4EPcJBTvMsSnEjh+o9Z7iXw3WzjGB/xF2lp8O673gLfTUICDBsW+njXr7e3swdbUXP3brn2W2+Jhn/zzeIEtiIpyfvvceNg0CD5Pz0dXntNav906CDlIQ5Y95I3GPKdQOad+4FWWutrgRuB75RSl7n2Fby3zpCnrGJvSMcnsIftyGJwDydsj9vNCRYvhi1bbA9hwYKQLg2IgI2Ls95Xt27Or3/zTSn09vzzEunTvDlMmwb33Rd8qYaZM8XMdOutMgHMmSMrkPfeg+7d4ciR4O/HkwMHYMgQiWj66afsmkMGQ24IaNPXWp9y/V4M3Ab8qJRqAi6VznDeUpbiIR1fjHBiiASgCqVsj6tGaeLjAztWs7Lgm2/Epn4qyFyvunWl0qY/TubPlwic33+3fu2CBaKZe2rj+/eLoL35ZqkJdMstkjF81VX2Yzh+HCZNkpLPvqxYAe+8E9y9eDJxIlx8sZinhg+H226Dq68O/rkYDL4EEvp/KqXGKaUuANBa/wE8CsxG7PyG85h+tKQS/jURfBufu7mUGlSgBACxRFseE004j9GKpk1Fk7Zj9WpJoLrvPim/MHJk9r7MTPjhB4mqmTyZ060QQRK7nn9esnOzcZCRAX/9JQLzzz/9r/fDD9ZC9ORJ+P576NMHfvxRNP8vv5SoICsuuEAmEDtNfNUq+3u2IiVFnNs7d2Zvy8qSFcVLL4V2LoPBTSCh3x+YBdnffK31ROBawKbOoeF8wImTV1jg1/0qlmiepA0XUtFrezMq8jZdTv8/l0TL86aSyXJ243CIIC9nUZstIsK78cmWLSLIly6VOvqXXgq9e8P//idJUp06wcGD2a+98UYptWzF0aPw6af+20+etD4evG33IJFGt93mn5xVoYL0AIiJsT9XoH1WjB0LWlvvs5q8DIZgsI3e0VpnAaMtti8HLBfShvODX1jPpywnw8eKl0oGV1OPIXTkc1aSyFFqUYYHaUExj4/SgQDtEDciEr1VK9Hon30W/vlHCq1FREjkji/HjomJZds20djdZGbCvHkwYICUWfjvPzGtBIq62bzZf1vz5qLtW3Hxxf7bPvhAir5NmyZ2+vr1pe5Px47Ssevzz/07doWHQ48e9uOy4oS9a4Rt2+Qa8fGhndNgMHH65zi/soHufE9dPqQloxjEXDI4M0/fFLSfwAfR1CewlhgieZzWvEtXHqe1l8AHqIO1RzWGCNpTg3XrJKu1aVMR0uXKic3atzCaJ7t2iVPUismTpaZO27aSNRsIX80doH9//3aLIJU4H3rIf7vDIU7V+fPFZDNhggh8gAYN4P/+TzR/NyVLwiOPSKG4UOjVCypWtN538CB06SL1hAyGUDAZuecwv7GRu/mFQx5mmAT2sIsTfMn1uT5vKv7dptykBdj3J9v4m710pBYL2e4X2tmNejRLrkbb273t2/Pnixnj1lvtx7R3r73z8uTJbBONlVD3ZO1aEaSlS4tW3q6dtFycNk38BO6VRNu2MhGFmi8A8NhjYnr68ksJ37zxRqkgGioVK0rC2dCh1klrK1fK2L/6ypSJNgRPjh8VpdQLFtveyJvhGEJhBMu9BL6bSaxjA/7B6VIhM+cqmG2pZrvvcmp6/Z9BFus5wFV8Q2e+5Qlm8DLzqEscl1GDSpSkAWXpR0u+50ZGjrR2aO7ZI+aMli2tr7tiRWhdrgIVRNu/Xzp0zZ4tgr57d6nD/957kuC1eLFEyoTaPD0lRcxVrVpJCOnatbKiyY3Ad9O9O1QPEDbxzTfQurXY/w2GYLDV9JVSQ4EKwHVKqfoeuyKB1sCLeTw2Qw5oC8EOcJRUZrKZBpQFYAk7eY0FLGc34YTRlmq8zpU0wjoM5SFaMo0NzPQpm3AtDejDhQBkksVLzGUymk0c9jIpnSKdFezhHpoxm7uIJOx03Z2tW+3vZ+9e+OUXiXGfOxe2b/eOzsm0WGQ4HN7HuLHaZoc7lv5MImLc8flTp2ZvW7dOVg5TpniXhg6WESOkReSxY4GPW7FCTFTVqkkVUIMhEIE0/Z+BBUCS67f7ZwZwTd4PzZAT8ViHg4QhJZBBSiL05md+YxMHOMVeTjKJ9dzKT5zA2uMZRTiTuZ3XuZJrqE8P6vP4zkZM4FbCXR+ZZ5jJMBaxnoO2PoQ5bCWTLK9Ca1Wr2t/P3r1QqZKYRXr0sBfcNWrIiuDGG+39APHxvqGbgZk5U7Jqc5s5O2OG/PiyZQu8/37o50tJcfDOOzkLfDeHD0v5Z4MhJwJF7ywHliulfvGsu2MoPHSnPn+xy297G6rTHVmcfcgytuLffHYNB/iIZbyItWpYjAivfQn7EoiqJvaVE6TyM+tyHN9hkjlG6umkLZAa+F9/LWUTfFm5UoT+gw8GFnbt20v8PIiG/txz/jbv668Xc9GECTkOE5CooT/+kOYsvXpJmYhQauYvXmxdVgLswy4DMXt2XMCsZSvORilpw/lPMI7cnkqp4XA6JMMBOLXWIVhYDXnBy1zOLo4zgXUcJplwHLShGiPpcVq73mYh8N0kBtgXiA0cYodFbR1fGlLudMKWm1KlpDBZz57WFSwPHoQ33oAmTezP62kjHzBAhO0PP4hWXaGCZKwOHy7O3X37jrJ8eRnbZupu3AlVe/ZISGalSoEbrzid8Ouvol0nJQVewYTqGwAIDw896b2avSvGYDhNMEJ/MHCF1npNXg/GYM0KdvMbGyhJFH25mNKuBiZhOPiMa3mB9kxnE3WJowt1vcwpvkLXk/IhllpwU5s4ylOcAwH64cYQQV8u8svgdToltj5QDDqIPdzKXh8V5R3l43CIpv/002LiKF06u9JlfDy8995mwsNbMHSotGA8nvNchdMpYaB2Qj8tTUoyzJ7tPzbfom8REXblIQLTqdNRvvvOepUQH++fB1ChgqyQDIacCCbQa7cR+AVDFk7uZwpX8DX/YwFPM4tmjGQsq72Oq0Ucj9CKq6jn16jkAVpQzkK416A0/bkkV+OKJ4arsTamlyGaTtRmJD24irpMZj27PVYFAwaIczJQFiyI09bKpp+WJiGKblJTRcNPTpbyCFaljZs3F3v9smUiyPv0kZDKK66wv747y9eKIUP8Bb57bJ5affnyEr554oQI5IEDAzuyPYmKcvLyy/4lH1q2lPo+114rwr94cclSdnf4MhhyIhhNP0EpNQGYCdmB11rrb/JsVAYAPuIvRvO317ZtHGMgs+lGPeJsHLmeXExlPuRq3mIR/7CXcBy0oiqvcAWVAxRG8+VARDIPMpWl7MTpOu+NNGQBiRwihVii6UF9vqYnyaRzH1N4nOkcI5VyxHAtilf29WD8+PCQImusmD1bEqAGD4bx40WQVqoE3brBRx/Zx9YrJTHvbtaulXh8K+2/Th3760+fbr/P4RBH9IkTIoz79RO/g5tvvhHHbq9eAW8RkMmpVSupKXT0qDSMf/RREfSXXy7afkqK+CFMz15DsAQj9EsDJ4C2HtucgBH6ecwMNllu38FxPmclz3FpUOe5nSbcygX8xS6iCKcFlUNqXZhEGk/VW8E6sr2ra9jPBZSjDnEcZS/HSeV3NtOf3zhCipej9yDJfMU/7Py9Fvv2BRe0Hh1tX07h4EHRtl9/PXs1sHMnfPGF2Pc//FBWE3/8AceONaZNG9GyW7TwPs8FF4jG7HYKu4mNDWwqCbRKSU2FG26QyKF775XVhSd790qbxRtuCK7hilLin7DClGAw5IYchb7W+l4ApVSc1jqXFcENuSEpQCLVSZtwSzvCCaNdLoujfswy1pXwD6dZi7cN5BDJjGIlxbD28a+ts4HIyAtJT/efcDzt9w0ayM80m95spUrBzz9bm39+/VXMPdkFyYqxY4c0Z/ntNxGinnz5pWS+/v671NFRCh5+WMJB7WjXzj4ip1KlbBPPkiXWx6xbJyaaYLR9g+FsE0xGbjOl1HpglVKqilJqk1LKogyV4WzThAqW24sRQRcbm3pesJbQgtdTbEo17Lp0LVHtrJvO3nefdKz67DMpwPbSS/ZmmoYNvcsNe3LwoHUFSrt4+ago0aTXrJFksLlzA5eDACl3bFde+cCB7BpBgfr9BioKdzZJS5MGMddcI9m9Q4aQYyRTsGRkiLnq1Vdlsj1Ts50hfwjGvPMRcAPwg9Z6t1LqEWAk5NILaAiaZ2nHAhL9NOobachlPo3IzyanSOdNFrKY7ezgOPvIwevqQzgOMq367DggaeTPcP91OJZWx5kZRqlSYmIZMQIis8P5adNGtG3f6pd16kipg3//9Y9gyYmNG3M+ZsQIEfxOp2S39uvnPS6QhLABA6xbBn9uAAAgAElEQVQzeE+elDDODh3gkkusq3rWrSvNWfKatDR5tjNnZm+bPl1yEqZOldXKtGliirr7bvEVBMvq1XDPPZJbARKl1LEjvPiiKQJU2AlG6BfXWq9TrnWx1nqWUioXPYAMoVKbOKZwO2+ziFXspzgRdKEuz2JREvIskUYm1/KDbU38YKhGLNuwya5qeBj+/JrwXxWvbbmJa66MtI3JHzNGzC2zZkksfJMmEprZrJlE36xZE5p26c7QTUqCb78Vodi7N5QtK3H6t93mncz188/iNJ440V/wL11qfx33KmTQIFm1rPPIY4uNlYbwoQjYUHE6xd/x2WfWK6KZM0VAr16dHTr73nvS2eu664K7xhNPZAt8EK1/1iwoVqx6wKgoQ8ETjNA/rJRqhqtFolKqNxCijmXw5DipzCeR6sRyEZUDHluXeEZybT6NDD4nIWiBH0UYaT4lGOIoxrt05R/28AHLOG7le3BARg/NT2OX0b+2vTM6IkIidAYP9t83eLAI7XHjAvfbdRMdLS0PP/tMmrIcdeWlDRkiE0mNGiLkfXF3y/IssTx9unXIpht3olajRpKT8O67sGmT9PC9++68r4/zyityX4F66S5e7P3/xo3SwL1TJyhhn9oBSMG8RTZtlBISSpGRIe+doXASzFvzCDAGaKyUOgpsBPqcyUWVUq2BYVrrK87kPOcaTpx8Wnk9s/iDHRwnmnAupQYj6E4DLNpIFQDLLMo62JFGFu2oRgqZHCGZBsTzMK3oSUPCCSOJtICvT1idRe/xUmQtVBwO0WYffFCKmQUqqVy1qhxXvbpo9p6F2w4elAnkyivtVw1//OEt9D//3L47V4kS4p9wU7EiDBsW/H2dKWlpEsaam+bpmzeLaerxxwMft3evfxKam+RkB+npRugXZoKJ3tkMtFdKlQDCtdZB5DTao5R6DrgTArRXOk/5lBV8XXnTad04lUzmspV7mcyf3GfbfzY/iQ6xxYKinGXt/qloa7u+myPRMKERs3ZJWOMlufQQ1awpsezz5/vvq18fbr45kWefrUVcnCRpWVXqTE8XTdwOXwG2bZv9sddcI7WBnM6CiZ3fuze4lY8dR4KIz7vsMvFLWPkr6tVLISYmiFhUQ4ERTPTORUqpn4ApwC9KqblKqblncM3NQICAuPOXn1lLloUgWMpOppCLqlx5QC8a+3XCCkR1Yi23ZwRotkIm8HVz2FiOU6f8TQ0Ahw5JH9yBA8WcEsh2/8IL/rVv3Br2TTcdIs5VNcou4gdkMrCKm3c4oGtX720VrIOqALGTV68uOQBPPJF/UTpuypWTsFE7ihe3L/McEwNXXZXzNYoXl+Yu0dH+1+7Va3/wgzUUCMF8u78BPgPWQCDVLTi01j8rpWqF8po1a6yrQCQkJJzpcPKV7Y0OYVXuJguYv2MV1fcX/OKnDNCncm3GVdjKyYgAMYdAjeQSXK6jScj0fx9qxAO1bV44phk8JZI0IiKLyMiNJCRkRwi98koNpk+PJyND4v3ffjuLyy47xrBhW4mM9P8Ili4NzZrV5MiROE6dCiMmJosLLzxGlSpS88D9OYmIaAxYx4FWrHiM2rUzmTcvDjxWXE6nk9deS8Lh2EaDBin8919x1q6t5TqP9wzucGSxbp3oUfv2SSXRdesO8+abQdZe8CG3n+9Wraqzfbv/zFS37ikGDdpGiRKZPPVUPXbs8H4WHTocIioqkWAu27kzpKbGMWtWPIcPR1ClSho33niASy45ec59L90UlXEHI/RPaa0/zt1wzg5NmjQh2ketSEhIoIVvimUhpzEb2Ix/pbEYIri6egu2VU+nDmVonoNzNxC/sZFvWcU+kqhFaR6hFa0IUALSgs9pwTMc5HtWc4wUMnGy6dBODpR1sonDhBFGG6oxJOYKWja3PndzLmIVKUxhg/eOyQ3gwetwC8yGDcPo10+dNoW8/753IxIApzOMP/6IY9q0OF57Tdomjhghmnvt2uKE/O237OOTk8OZNSueBg3iuffe7M9Jz57iyPUlIgLGjCnNTTdZ3YmDdetK8vHHjZkzR7Jst2/3P6pkSTh50n/hvHRpPBER8SF3z8rt5zspSTKP//0XduyQmPyyZaXy6KhRxYmJaQRIrsPw4bIyKVFC+u0+/3xZIiLKBn2tFi18nezx5+T3Es5NeQL2405NTbVVloMR+r8rpR4Dfse79o7FR98QiAdowbz0LZyI9Nagq1KK+5jCbk5QjAjaU52R9KAuoeXZj2A5A5nNCQ8H6gw28yXX0Y36AV7pj6Icr9Lx9P8JiQm0KNuCAyQRhoOyHkuWBSTyKcvZyGHiiaEnDXmUVkzgVj5lBfMyE5k9G5J+qwmftoLMbOFYsqS37fuDD+zH9PvvEqrZp4/UzXHjG07pZsoU6NUr+1qvvSZdpjwVo6goSbZasgT++8/+2n/9Jaam1aut99uZn06cgAULzqxlYrBs3SrP52+Pck3x8WLm6tvX+9gmTbwL1xmKDsEI/Ttdv5/y2OYEApSkMljRgwa8tP1CZtQ9zDoOUJpixFGMZew6bTdLIYPZbOU+JjOfe4KukZNGJh+xzEvgA+zhJK/xR8hC347yPqWaZ7GZO5nEPg+//By2MoZ/mEgvnqA1zRe2ZnI36/Nt2CCCsVQpcagGqm556JAkZnkKfLBvXrJjBxw4kD0jlCsn2bojRogmHBsrmvtFF8Hbbwe8bTIzAzt77RqTR0SIVp0fvPyyt8AHSWD78ENJpAqlx7Dh/CWY6B07y2yu0VonAkWyEGzno1UYyLWkkUkkYVzGV5aOksXsYDZb6ELdoM67hB2sw1piLmIng5nHq3QkiTQWsYNqxHKBq0fuP+zlQ/7iX/axjyQyySKOYrShGq9wBdWw7wLyAUu9BL6bFezhUkYzjpspV66GbQG1EiWyHYIREeKQtatro5RozcFSsyZUqOA9I8TESDy6Lz16yErArt5+hQpSJG3GDOtwyHr1JH7dd9+ll4rpJK9xOu1r/fz7r6ySunfP+3EYCj/BRO+UV0qNV0odVEodVUpNVEpVzI/Bnc9EEY4DBwdsIlczcNoKcSvKUIzIAG/ncJZwD5Nowgi68h0tGUU3vuMn/uN6xvIV/5DAHnZynD2cZC0H+ZJ/6Ml4kkgjzZFJArvZ6ZNpuyZAXZ4dnGAIf9C4sQg/Kzp0yI6acTgkgcqKEiVEKw+lbkzPnhATE1zAetmyUKuW9T6HQ+rx9O1rXYO/UiX4+GNJ8nJHERUrJsJ+9Oj8C920Ckd1YxdXbyh6BGPe+QxYDDyATBIPAqOBHnk4riJDHeLYYJHgXIooOlIr6PNcSEWaUoGV7LXcf4p0vuHf06uKZDL4nc2sYBeHsJekCezhJn5kXaO9bCeJUkRxBbX4gG7MYjMnc0jAWsYujpLC++8X86rVEhYmNeF9i6C98or0x/32W8maDQsTu3/p0pJgVayYdWJUpUpy3Pbt0jawdWsJLezbtz7JyRJGOWqUdZ38TZtEi/f1e0VHS1btbbdJdy6HQ8o0PP20JGwlJYmt/sknpfJmu3YSPjp/vjiYmzcP+GgCcvRoOE8+KTkMYWEyaQ4eLII9KUlCUj0nE4dDTGRWREZK2QWDAYIT+nW01p5x9W8ppe60PdoQEvdzMYvZ6VeuoDv1aUrwCyoHDi6gvK3QB+t420AC381MNuN09Ws5QRpT2cACEjmeg8AHCCOMMBw0bSr1ar75IYttWx00v9DBDTf4a8ELFojt/oILxFa/bRvs359zm8Njx8Q2//LLkjE7bpzbuSp5BBs2iG19xAh/p+Ybb/gLfBDtf84c77r1cXFSliErSwSwrxM5Lk4mEJCIoh9/FCHdtKlMDnaC2ZOTJ2HAgHpeY1q4UByvDofsb9pUisHd6fFN9I2bd5OeLs/DM6s4NzidEjFVvLg8G8O5STBC36mUqq613gGglKoBAQq9G0LiJi4gjSw+YwWaQ5Qhmquoy1sEbwjeyCGmssHWVHSmWE0WwQh8gDZUJZZoFrOdoZGLSLh7N5GEs4UatKYzVT2Su8aNE0EWTFZoTIyYetxRM8nJksT155/2TU7S0+GZZ0Rzd9eXOXVKBKoVu3dLlc/+/f33hYXZO29BOnsNHZrtx5gwQer4/PqrfVlmN++/D2vWlPTbvt8j72npUinkVrZstq0+kBnp0KHA18yJceNkXKtWybNv315qCtXLvwrfhrNEMEJ/ELBEKfUXElzdGjHxGM4St9OE22lCOplEEBZ0xE4WTh7lV8axhqMhNlVxE4GDjAA5d2E4yMplTp6iLK/SEc1B7mCiV+XNbaxmE4f5g3uJQloofvBBcAIfvAW+Jzn13j16VGrAP/SQFF77+efAZQuio+U606bJpBIWJr6Czp3tX7N9u9j4fR3Xy5dLIbQPPww8xn//DbzfzbFj4jNwC/3GjbFMrCpZUspDhMKRI3IPe/fKqmb8+Oz3JiVFcin27pXCa3Yhs4bCSTDRO9OUUhch9fMdwMNaa5NrnQdE2nSc8uQIySxlJ/WI5xfWM5IzyyJsRVV2cZwdHLcU7aEK/GuoTwWKU4MyPEFr4ojhUX61LLX8F7sYzUoeoRU7d0oMfX5w6pSUPc4pTLNuXbjjDimg9t132U1RPv9cJo0PPrDWrr//3l6znjMn5/GFUnZ5x47sv59+WoSwb02cW24JLU/gzz/lngOFqIJMYm+/LXkOhnOHHIW+UqoM8BLQCTHr/KaUel1rbVNn0JAXZOHkGWbyI/+xixOUJJLoAJNELFE0owKJLoFuRQRh/M1uUjzKI1elFHs5Eahyji21KcN4bqYEEo5zhGSeZiZjsc4MBE5HKBUvHlpt/BIlctbqrSheXBqYXH114OMqVJDaP5MmSV1/z7GlpUlm7zXX+NflgcAVJjdulLDPbjZ5CyBCeuzYLNLScm5IUtkjefvCC2W8774rPoqSJeU6zz6b42lO43SKEM9J4Lt55RUx+Xz1Vd72CDCcPYJpc/MdkAHcAdwLlAS+yMtBGfx5nT94j6XscpVxOEl6QCfscdJYyyFbgQ+QQZaXwAfYFYTAv5wa1KGM17YSRPIYl1CCKBazg75MoR4f8S5LOBJgnCVOiqQoWzZ4oREdjW3jlUA4HGKfL1lS7PV23HGH2MzvvFPi260mo7Q0+5LQt95qL/jT06VhSSCuuQbuvHPv6UJxYH2+EiW8HbkgDt6vvhItfN48ySIO5Hvw5b//JPs4WNLSxFn96KPBv8ZQsARj06+ltfYMzxyglLJX3Qx5wuRcVOE8RN4sxrpTn9towvv8xSYOEUcMvWlKV+rxIUt5njkkE7hYGwCbyjCiyyVMLSF24mAyRuvUke5On3yS87Hh4ZCVlYnTKSeOjJSQ0YgICXk8ZtHcq0IFsbmXLSuO00DmGM+4+KwsOXdmpjiGA/XHXbVKhGVkpNjG586VieyeeyREFOCRR/bw0ktV+OEHGW+HDuIPmD9fzFOlS0vI69lOuEpOts9wDsTMmdlduAyFm2B0gP+UUqd7/SilLkQaqRhySRZO/mAb09lIahDC0YmT/YWo/cCvbKQmZXiPrkzlDr7hBrpSj/Gs5mlm5Szws4C/qsBD13IsMYb//hM7tLublS9hYdL8ZNEiMY9s2RKcYIqP57TABxG0s2eLSSQx0fo1mZlivwe4/37YY93HHZAm6Js2iZO3TRvJDWjbVhqRB8IdOXTbbRLe+cEH0hS+bVtpWeimdm3pwztwoJz/qquyfQjHjsmE0bGjd1SPm4wMCRV1r1IyMmDXLpkwAnHxxRL6akVJ/4Ci0+zfn11C488/pSlNpUpyDy+/HPiahvwlGE2/IbBAKaWRSugKaaG4FXBqrU0NnhBYVvIA/fiCFewmC+kn25ZqPE97LraprinRPNYG73CgJNEcy2X0Tm5YxHYGMouHacHHLGc/ScQRwxj+IYMgMmB/aQA33wbOnKOUqleX8Ed3N6rt20UzzonKle0Ftl3XKxAH7DPPiIlj3rzA1/jlFznG6fTOI8gpPPLyyyUy5scfvbcfOybavDvO35MjR2Ri8O0QtmyZ2NXdK5/kZMkHmDNHJlGlJEt47VqZoMqXFzv/hx9Kopsv4eFy/48/7n0f1avDyJHyPuzb5/+6UqXkOtOmlWLgQO9n/Prr8pzsWiwa8pdghH6QrZINgcggi5eZzbv1lpPuIRh3cpyfWMs0NnAN9fmWG/2amJwi3dZU4wQG04EXmUNqrtyvoZMFvMViPmY5p3KTsrG+XI4Cv1cvuPZaqRrpKZzefTewAzcyUoRqzZqSRJUbMjJg7NjgjrUyEQWiSRMRqvffb73/+HEZ940+bYa++87eD+FZc6d3b3HmuvEtYLdjh0QfJSVJlFFGhpw7MVEcwTfcID6NOnUkHHTfPukf3L+/JLeVKmUt9NPS5FoffVTVclJdvFhyFEINHTWcfYIR+nuArkCc50at9Td5MqLzlHv5he9YjV3ATTIZTGAdJxnHw7SkO/VPh3CuYi+nbEwmWciE8g5d+IZ/2cxhjpEauFWhB3WI42IqcYJU5pHo1+g8ELkS+AApOQd2d+smAsxNejp8+qkIIjsqVZKM3gYNJP7+q69CiwjKS6KioEwZMU+1bh3YPJWQ4C/0g2HFCv9eBHZMny5O6hdf9C+N8eOPYk5qY1ES0W7cSUlSytq3MYsnfftK5NQDD+RPqWmDNcHY9KcDTwIdPX6uyMMxnXesYDcTWRfUsTPYTE/GU5Zh3M0k0sikOqUDvlFf8zePcgn9aEkMkUEL/L5cxBoe4SdupTN1QxL4uSbVAYtqBDwkLk60y4wMMRO8/LL0ux0wILCWf889IvBBhGZhqjeTliZ279RUEZCBCqCVK+e/rU8f/5aQbtq2ld+zZgV2IHty5IisONwCH8QZPX++mIfsKFPGent4uKwOwsLsP3v79okZqnNnWWUYCoZgNP1yWmszL9uQSRY/sJoEdhNLMR6mBVWIZQk7+Zi/2MBhjpJsq6nbccJVIG0TR5jDXVQl1jb8cgOHuY0J/MTaoFOpShHFE1zCUBaylJ3sJRdB77lhVSWYG9gNdOSIaPqlSklUT07ORxBbddeuokVqLcKpZEmIickkOfnMCsmXLSsmJocjcJ/ds0W7dv7b4uKk6NugQd7+g9atxeexYoV397CcKFFC6hFZsWCBPHcrm3/XrhJ95Evr1lJVtF69ZP79N3CBoYMHpUTFrbfaZ/N+9ZWUwNizRwro3XWXmJ0MZ04wQn+uUqozMFdrnQ+q4LnDCVLpyTjmknh622j+5n4u4gv+ZrdFa8RQWcwOhrOYb7mBKxljqYtn4mRCCAIfoAM16ctUlhMgYD0vuKtnUIelp0sDkGA5fFhq4vs6On3taa1bSyz7rFli37aqje/LoEHi2Dx+XISebxz7NddIWOWUKWduTmreXMwgVgXgHn9cxv/tt+JLuPBC8Q089JDYy4OZHN20bGnfm+DUKVmRWAn9116TiW/qVAnRdDigVSsxvTkc8P77m7nlluY5OrPXrBFnc7duMol88olEZZUvL7V9fvghu4zFf/9JRNCxY/DII8Hfo8GaYIT+NmAmUngNpBSDU2td5PvwvMRcL4EPsJsTDGcJSWexJt0SdvISl9OZOszEulBMKLNxnKtj17R8jryN2V2G5A0WtouzgLvkcE4cPCiRKxERIrw//lgmgKNHrSeAli1FqDocEhs/fbqUHli5UmLrO3eWxKTjx0UjDWYMnlSuLDVs3EXM3nnHvlomiNBv3Tr7/wED4Kefgr9e+fIS+vn++3I9q4Y1TZrIvVoRGSmmmVWrJIqqZk2pReROAIuNzWTnTnkmixYFDq8NC5MS1X36eJeTcDj8J89Tp+CLL+S9CCXZzOBPMEL/QSRBy/TE9WEh1o/kbAp8gD9I5A4mePWlPROOkCJOZRsiCfOKMMqJ6pQihkjLvgCe5/yiZDcG1Q4LWOAsr9m8WcI+69SRqJdFiwKvKFJTJeTwqafExBIXJ6WYfTl1KrhVgycNGsj19++X1o3Vqlkfl5Ulwjkmxr/Ry6xZwV+vXz8JCXWXin7oIfGXeK4QypaVFUVONGtm74wtVizb4d6rl39oKsiKplMnuP56b4EP9qultWslgsnuORmCI9jonTMszHp+kpZPIZInSGcsAbp254JAVohQBD5AOGFcRd2AQv8hWnJHrOLCyfJlt0ooyg8qVZKs21tvtS+j4Mnq1fIzebJo4WlpYnP3rLEPorE3ayblG4KhZEkx05QrZ+24dTN2LAwfLr1vo6Lk2kOHikklKyv4kNGYGNGoPcf95JPiHP7hB3Gy1qwpjWquvDK4cwbDkCEirD3NVZUqZSds+fb0DUSZMvYrEEPwBCP0DwFrlFKLILuIutb6vjwb1TnCBZTnP4t2gcWJCNlxey6TyDGSyaAM0ZYlnuOIpht1GMAMaOKkaaf2zBkbRDeRPKBbNwlVnDw5tNetXi22+4wMqFJFJo3hw7NNDQ6HaMhaB1ceunx5Ke1cvz488YQ0jfFl8WI5pzvWPiVFTCp33y1JWSVLSuz8rl05X69nz+woH09uvVV+8gr3aubDDyU5rFw5scvXrSuTViBTli8dOwbXhMYQmGCE/q+uH4MHGznEDKxLETamPNUozW9sPJ0wZScQG1KWeGJYzNkJC4mnGBGE53vZhkSOEk0EWNzjEVK5nvGnQ0nDqpYBLCTQWaZUKcjKSicpKZJSpaROzaefioabG4erOxxy926xiZct611i4PbbRZiPHi0ZqFZJTCATxNat8jNvnphoxo2DSy7xPu6LL/yTq0Cap4wYIdUzH31UtGVPE1VYmEwGUVEyMXTq5F8KwV0wLiNDwlutnLZni9hY61IM7ryArVv997kjt9LT5T46dhT/i+HMCaae/hilVBMkNj8CmK+1/ievB1bYeZLfOWHTPeoAydxBUxoQTzQR1KA0l1OTXkzgb492hpGEsY2jbOAQUYSdlTj5FlTmSmrzAkHUKjiLlCGwyuaZO5B10xr4tAWcisqTsURGit29aVMYMCANrSM5cUKE4+efZzdiP1MmT/YXZp07ixliyhT7sfk6NrduFdORr+17r33ny9Pa/Q03iLb82WfZmnTPnuLgteukNXasmF3WuVJH6teXCeSBB+yvl1e89Zb4WTy7l9WvL/2M09NlhXXxxdYN6Q25I5h6+ncC/wf8giRzTVRKvaa1zmWS+7lPMuksDaCZJ3KUJ5kJQAPi+T86Up+y/Mm9PL9zIjOrHWATh0kn67TL92wlRs1iK7OxUJ3ymEmsD/4O2uyGAUtgWHvIzA4Cq1BBNPAD/hazkMjIEK1w1y7IyChxevuGDZKB+sILEr0TbCKTHXaa/LRp9uGTdpEsVrbtQA5LT4du9+7BV9vcsEEmBE+fysaNkgPQvLn4CvKTChUkIez776VjWKVK8PDD2cXdugTfNdQQJMGYd54GLtFaHwJQSr0OzAeKjNBfQCKTWA/A9SguoWrQMfEbOMzT/M5l1KAasZTMjGQjh3PZgDA4CqLyQFAC3wlMrQ+TGkF8Mtz+L8UmXESPHhJGmJkpAvlMcTqloboVSUniCD1TgQ8SAXTwoNjZ69aFFi1ke4kSgV9nhZV55eGHs9sSetKsWe6bnI8aZe1EP3pUzFL5LfRBsnnvuiv/r1tUCUboh7sFPoDW+qBSqkgkaTlxMoAZjGIlKS7H7EhW0JeLaEll25h5X/ZwkpGs4DWuZGaZXQUilAsFDuDfivC1q3ZvTCopKbJ8v+suKcNrV175bBJqxy2ruPFixcSscuGFkjUaHQ2XXSaVKPv2FcelVfZupUrWZpvLL/ffdvHFYo565x2pxxMdLdE7b70l0Ti5IVB4arD9iQuKrCzxpcyYIYlhjRtLKYmGDQt6ZOcWwQj9VUqp9wF3qav7AYtE7POP39jICFZ4hTCmkskoEvgfHVjHwYCdqTw5QBI3Mp5/Ygv5NyuvqechdZLFD7Bnj5gXAtWuz2+KFxchU66ctfB2OqVxiJvUVKnV37evOGffeEOic9yVMaOjxWHapw889lh2M/awMAmRHDrUehw9esjP/v3ZRdvOBMmvtKZevTM7d17z8MMyCbpZulSSu375xTr6yWBNMEL/AcSm/yWiq80F+uXhmAoNv6AtY9YzcLKFo9zLRbzPUo4HUct+BbtZyV55gkWZOP/WiR99dHZOHRvrXZfmTGjfXgS+XYnlVJu3fPFiKS9w550SHjpqlJiUunTJLgDnXhEcOiQmoZtuyjnLtEKF3N+LJ/37i8PYs9AaSMeuvn1lostNxmty8tkxmdnx778S4eTLxo2yEsptGe2iSDBCPw1YpLUeqJQqh9TXz6fqXAXLKZvoHIDdHGci64IS+ACrsPH6FTWKnX3JEB4uZpORI8UnYFW3JhQcDgmjzE1YZ3o6rF8vUTzly0vnK19KlbJvVr58uZiGVq5UVKkirSH797ePxAmVEiVg4kQYPFgykjMzxVSUlib2/GrVpDH7Sy8Fd83Nm2VFs2iR3HvduvV4442zm+AF4hy3a8e42j653GBBMEL/CyRqxx2E1hFoDeTSlXRusIq9AfvSHiI5pG5VwZY7Pu9ZVfGsnzIzUyJ1rr9eauU0by6aYahlEdycadG03JYJWLJEyhZIWYKSrF0rTuLNm8WO/9lnckxkpJh8br45d5NBzZowZoz8PXCgnNvN4cMyaaanS0euQKSkSGKX56rh4MHS3H232N0bN5aopIkTxTR1zz3SgSs3BMrEzY3jvCgTzEKupdb6bhAnrtb6TvIjs6aAuY6xAWvo7ArSlm/wYH1ZGHZpnp0+K0syVatWFdNKQeHZySoU3n3Xvw5NVpZ0trrqKsnOHTsWvvlGJocWLeyjlAKN7b33ZCWTlAQTJvgfk5UlJqBANf9BTFe+ZiIQH8hHH4nv4vLLpTLn4MEy3vffD228bu69197nYMI6QyMYTT9MKVVZa70HQClVgdCKOt5MHnwAACAASURBVJ4zHCeVT1jG3+xhew5C/Qj+tmlDDiSWgsYHYHcseencWLAgO3yyINi8WcoBN26c7fDVWkweTZrYv87OTHHokH8ZZKdTtOjmzWWyuPfewGM6flw6kc2eLRp6ZKQ0QLcrfrd1q0QZ1QjQ72aTdUI6IJPLf//JKszNgQPw6qtSnvrgQRlTp07BZQOPHi2rmrCw7BVcTIwkpz3/fM6vt+K33yQ/4NAhKF26OkOGZDfhOZ8JRui/DvytlHLnzLUGnsi7IRUMy9nFXUxifZC15bJwoiiL9jnevoV5EccJdEuEjtthfi249WY4nsu4wxw4efLME7zsiIgQe/3y5fYN0H/+WerbX3SRaMv//CPCr1Qp6fv71VfeWcE7doi5JTdO6KNH4X//E2dwbKz9cb17i13cTXq6rIo8hagnFSsGLgQHUmTOjuPHvQW+myNHsgvuZWaKkL3iChmHwyFmq6uv9jZbvfeeCHbflccNN+S+A9d770mfhOxS2BVYsUKKz3mWrj4fydG8o7X+AbgYGAt8gyRqTczrgeU3LzInaIEPcDFVmMrtdKc+ER6P0Qh8G9xf4ugs6LoFPvg9Ty+3dm3enLdxY9G6c2oSkpIi2m5CQrbwO3FChIqnZjp8uMTjDxqU+5DVHTtkIrFj0iT7rlp2fo/4eKkldP312dVFfenXT0om+BIXJ0Xp7NizJ/uZbNggZqKRI6WeUM+eUpDN7VfJypJqpFbXX7Qo9JwLkGzpjz7y732wZQsMGxb6+c41ggrO0lrv1lr/rLWe4jbznE/s4jiL2JHzgS7KEsMAWlOPeFLIIOP8tHblLR0SITJ/SlOfLWJjRUglJ/vvc5cNCAZ3fP+qVVIDx6qoWqikBLA2Dh8eulP7n3+kftCUKRJpVKWK1BnyvE7p0mJ2ufTS7LaHNWok069f7mvlpKfLOd21iw4ftjcjbdsmDvtQmTzZusgbiI/iTB35hR3TgwZJuAq2hnw8xbiJRrSnBp35hrkFUOfmvCA2NU/CN/OK+HgRblYCH+y3W+Fu5DJmTPD18ANRrJhoz+vX++87dOjMQ1jd53n9dTGpeMbjX3aZtDL87DMpcLdrVzSvvy6rizqBWyHbkpGRbYqKjZXQVyvKlMndNQKZwaKizl54bGHFCH2gNmVoSYD1qAeHSWEUK2nJZ36tEg0hsDkOTuRNlc28IDw8cDarlf3ajpQUafIdSk9bN1blF1JS4IMPoE0b//j/qKjQatbnxIwZEk3kyfHjMiGsXg2ZmSJS1q2TKJ7bbhOtv0sXMd0Ei3tlEhVlH4nVpYvkZ4RKt27ib7Hi0ku9Nf2kJDFvPf64+AFCmdwLK7aOXKXU4EAv1Fq/evaHUzA4cPAc7XiYaewnuG/i3iCPM9hQ5QTUOgKJ8TkfWwg4cCD4rljBsHixfbtBO4oXFw16zhx5/V9/eVftPHZMhH/r1hLDD+I8vvRSeZ0vdeqIs3bZstDGsWSJxNy7+fRTWb34kpYmGbMrVsj/6enQoUNwIa2dOmX//e674g/59Vcx95QsKQL/s89CG7eb8HB4800p65CYmL29fHnJi6hdW3ob3Hyz5Cp4+oe+/lr8Mo0b5+7ahYFAmr4jh5/zihtoxHT68BAX05naVKYk4effbRYeqp2Ep3MZ0F5AJCTkXOgsOlrMB7VqSRG5qlWtj3M6xeF59dXBX79zZ4nXHzZMhJJVmeb0dP82kMOG+YewNmokZQ1mzBDHcteukv374IPZ9nk7fJ9BIPPRxo3Zf0dGSmRTnz6BfSD16skKwU10tOQm/P03jB8vkVMTJ8rzyy1du4r9fsgQaURTqVIKBw6I2W3bNmk2f++9/gEB//4rCW3nMraavtbaMh9PKeUAaufZiAqQi6nMSK4FpMLmVDbwBNNJ5CwYXg3+NMmjuMo8Ij1dMkq3bIGu4T8wL/NG0sgOMo8ihSsyJlK8+x2MHy9C7vrr7dsZ1q4t9f3r1bOO3ImNFTNHfLwI/A8/zN4XyDTku69+fYl0GT1ahHD16lKa2Z3J+uab2cc6nSLUR4ywjpgpVUqEtifh4f7HucnIkHO67eSVK0s0zpIlYmbxDVONiRHzkVX9nxo1AucNhEpcnDinhw+HvXv9kwXsnvGiReLjKFv27I0lPwmmicqDwDuAZ7LzVqCQ1+Sz5zipPMRUEjlKYyrwMVdTjEgyyGIS69jBceoRzyTWFWhkTtfxq5l3fSPSimW/TVEpGXScvI7fezUtsHGdNQrQpp/bJipdusCeb3/gZcddLMwYy/OpP5FGMaJIYWj0LbSPmM47C+DUqTvo39+7I5Qvn3wiUSR2oZr164smXrJktpa8ZYuYIawqf7qxMhtFR0uIpRVLl0q8e0qK+AXeeUfKKwwYIBFGbuFftqyUMm7Z0vv1AwfK660ihOrWtXaMtm0rfoC33xbtGkSgDxyY/3HyVg7wQKSk2BfcOxcIJjnrBaAZ8BrwEtAdyHUuvVIqDPjUdc5U4H6tdYDcvrPDFo7wMctYwg6Ws/t0LZyl7GI8a7ibC/mG1bYtEPObruNXM6TvLyz8cQ3Pf38LacUiiErJYGjvn2j/uzyuc1rwZwDTCi79MSeBb1VDH6Q8wVNP38iS4WNpHzGdodzCoNTvGBLdh/YR01mYcTVzw2+kTx/vZCgr/v47cMjhiRNic1+/Xmzoy5blHE7YujU89VTgYzx5/XXR9N0x6198IaUZJk2S661dKyYVd6MTz45dbho3FjPVrz6dtMPD4f77s/9PThaTktMpZST695cm7999J8+7d++CaXweqsYeFxc4Ma2wE0z0zn6t9VZgNdBUa+0W2LmlJ1BMa90WeB4YfgbnCoq5bKEjX/MeS1nKLr/iZydJ5xMSCo3AB5h3fSMWdq1H+983MbT3T5Q4lnJa4C/sWo951zcq6CGGhucjPxIFI1rBqAKslRCAsDApZWxlYjhyBCZOK8amO39iYcbVtI+YzrwSZU8L/OdTfyKyRDHmzAnuWoGifg4eFCHbpo04bQMJ/AsuEGH/yy9ip774YjEbXXedlF6wYvNmMW34JinNmJFd3/+CC8SZOXiwtcB389NPIuArVkwlOlrCN998U6JeQEofN20K990nJZwvvFASskqVkmSshx8uGIEPcv1y5YL/7sfEnNthnQ5nDqqDUmouMASIQQT2YKTUct3cXFAp9S6wTGs9zvX/Lq21pbsrISGhFpx5IPzD9ZewIjb4bNvCgq9mD7Cwa73Tmv85SZpDiq4N7pTzsR5UrZrCnj3RZGUVhm+bk+ee28bI4aWZXSw7iLxj0iGSKE27dkdZvPgMu53gJJR4iR49DvJ//7eNF16ozaxZ3hFR8fFpvPnmVlq08E5fHTWqEqNGWXuaW7U6zogRGy33BSIlxcHx4xHEx6cT4fqIbtpUjAcfVBw/7v2ZLVkynU8+2UTjxgUfCTdnTmk+/7wKmzYVByAuLo0jR6zNj40aneTbb+0r8BYyardo0SLRc0MwkuMxpFvW00BfQMP/t3fe8VFU6/9/pxKS0HtAQC9wFJAiRRCQooDYGxcVsXevFytfu9iuDRWxIyqI7Xe9YkFFvCpcFVAIiorlgAqIovQaICHh/P54dt1NdmZ3dtnNbpLzfr1WzM7OzJnZ5DlnnvJ5uG0fBlEXykVGy5RSmVpr1wfuzp07U6tCsvHixYvp4UFV60928D2JLflPFCU5mdzy3EnMafnAX+/d8txJVdfgA2QbOOM7eLQXrA9TJRNEy5Zw9NE5TJmS4LF5JDs7jfPObE6v10dCUOvDO2udyft9X+PaG+tz7LGRVSrDE93ktmBBY044obFj0HjTpmz++1/FRReVf79ZGJXr/Py6nv6+/BgjTy1ff72Yfv26lNv2wgvOukI7dmTx+ecHReyPa4y4yr76StJMTzuNvyaU+LGYq69ux6xZ4rPv3z+bnj0Dnc+CGTIkP6p7k0jc7GBxcTFLXdKqvGjvfAdcB3QDbgcaaK1jFEgFYBsQ/CCXHs7g7wsllHEV77MzjERyKpO9u5Q7zyufYH3neW+QvbvqVLI68rfNcKy3VWRamvyR+33BiSDaTlG5WbvZOnYkbf6cxRdpIxhctPEvV88JX47k2Sd3x63TlVc2bnTPEoLyqZN+Ro1yd6l4Dabu3CkB4oMOkkDspZe2C0kZDdf3OFJF8qZNEjw/6SQRlhszRoLAidBWyswUQbyRI8VnP25c6P3p0ydyn4FUJ+Kvu1JqKPArMBmYBvyslOq1D+echwSDUUr1QWIFcaeEMgYzlVf5LhGHTzjBrp3Phrdj8O/XlfPxV2nDvzMTPvaW9WuMpA+6dU3aVzIyotelOXT3DNIWzuLL7BFcs+M1iqjH9cXi4+9VNostb8/gt99i9/tGypOPheDeulu3wmOPSSHSOeeUV/wEaRV5ww3ejnvGGfL9aC1ZSIsW1eP882F20MN1uP614aqcAa66SorRgmMfhYXSbzjcZBIPxo6VvgOXXSaTzQMPyD2rqqmafrw8JD0MjNBafw2glOoJPAX0DLuXO28AQ5VS85Fn2AhK4NFTTCkH8wTLqbpNyAe/9cNfBt/vw7/+pZF/TQRVOm3znfawynslbixyBV5o00aCj/feGxrM9JOTEypmNrvsDNJL4KOiQJ5+CTlcX/wag0tnMLvsDEAmrIyM0GCt03vBtGrlLAjWooWka+7ZIzn2e/ZISqUXTjhB/n3sMemU5W/W0rKl5N2Xlsp19ughzU8iFaGBaO7MdvCcbtokQdrhw+Vnt768XbtKWqgbJSWhfQT8aC3poBdckFhlzEMPrX5Sy16MfrHf4ANorQt9BVoxobXeC1wS6/6RKKaUOtxdRR06AfwGPThP32/4q7TB/7wALjg+2aNAKZg7V7RbFi6EmTNDP5ObK8bVScFy1p4zQt4rIecvg+/HybiXlcnK222let118OqrW1i8uD5FRTKGoUNFoC1YLGzJEnF1hFPYBMlEuvJKWSHffHN5l8rvv0vmzfvvw2GHld+vtFTULnfulJV1bm757fPnu587uDlLXp4oW952mxRlGSOGdPz48OJnxcXhpZM3bZIJ7KOPZFKJVeCtpuHF6H+ilJoCPINkV58GrFRKHQ6gtf4kgeOLml48VeUNvh8nw16Sk1l1Df5v+TDoHChOgP8iSrp1k0Cm3320e7esKoODrzt3OksdgPh698Xl5GbMOnUSKYTevX8mI6MHCxeKgezYUTTzFy4U43vmmSLFMGCAuCDCUVoqTzJTpzr70Ldvl2BrsNF/6y3R+Pd387rtNnF3+FMwQYysWz1DRWXMVq2kIjga6tSRTmNuq30/ixeL++XTT6OPz9REvBj9br5/763w/u1IXlmc+97HziZ28W0UjVAslcy/BqSEwQdZGc6ZI4alb18Jzt1yCyH59U5GPyNj39UW3YrD8vMDsgbduslr5044+ujy+fZTp8qq/emnJfd93jz3CWrTJnlKCDdJBWfXrF0rLp7gfr2//AI33SQBW39P2lNOkYnniy/KHyszU54M4sE//ykTz6ZN4T+3YIEUlJ1ySnzOW52JaPS11oMrYyDx4Efi0I3CkjiKU2cZZoy07Fu3ToqUXnklvJ89N1eeArKy5BVLa0MvbNkiY1u+POev89x3X2iB1fbtElg8/XQJLs6ZI1lObm0id+4M36wlOIbw+OOhDdpBnk5efDFg9NPTZfX+j38EJp1mzYq54IJaXHppdNftxskniwvo6qvdewiD3LNly+JzzupOOGnlyVrri5RSc3DoAqi1TpkVvp+DiNDU05Jcft3XgqXEEUkP3x9MLi1NrKZ63bqSFrhoUceIKaobNsgK/uabZcW92SVvoXZt8auHqxL+6SfJSS8oCN8KsuKKu1MnmXAWLpSJonHj7xk40EWsPkaOPFImmnBG39/o3RKZcCt9v1r1+EoYR1xoQG2akMt6q3WfenzZHBbsl+xRJAWv4m4NGkg+vQR4veVK+NNNzz/f/Ry5uWLww4mEbdgAs2bJcQ4OEzJq5yKz2Lu3vBYvToxAoVORVDADBwayhaLhjz+kR+9PP7Vk1Cg45piqLbHgBdfnba31Yt///gQcrbX+H5Kvfz4QpS5d5bGKMDlgluTxRC8oimMLpypCrVpS8FORvDxJo+zZUwzpscfKSjWa3PMGDSRPHsILt5WURO7Dm5UlKZAgMYK+fUM/0759+BTLRBKuQ1bHjlK8F62xnj5dUlTHj4cXX2zOSSeJO2nfKqlTHy9O1hcBfwLWGuBTYHrCRrSP1CYLw20MwKV7hSU5/BJ/107FoqJ4U7/+vjXqAFldf/CBZMZ06yZZLIMHSwD2zTelIcjy5ZIyGo1POjtbiob8K+9wRnH7dvfm4n769pXVsv/Yb7whxr9DB9H9HzlS1DbbtHE/xuuvwxVXtEMpySp66KH4VVFffrlzQ5r99pPspWgLprZtk8B0sKx1aal8J3ffvW9jTXW8ZO801Fo/DaC1LgaeUUrFKUyTOD7hAuawggf4jFn8EnkHS2JpEl+X20EHiaGbMyeuhy1HvCo+i4oku+Rf/xK9eKcV6Y4dkTNU/AwcKCvzTz6RrJ42bcLr6IC4R3JznQvd+vaVtNXgcTVrFl2K5SuviFLmtm31AJnA5s+XTKB4FE+1awfPPCMG+YsvZKx9+sgqvcBbe+tyPPusc7AapH6jOuPF6O9SSo3QWs8CUEodCbjUL6YWg9mfwezPWrZzOq/zFX9SxB72JLExSo1l9Lfwbgcois/y/NRTZQVdVTAGHn1U3D0XXBCq6bJnjzf5hWbNZNUeTmfHjQYN5CljyRJJCy0oEJmDkSP33Y/91FOhGU1790o/2RtvhHr19u34IJr9Rx0l2Vbp6fIEEuu43SqwIXKxW1XHi3vnEuABpdQGpdQG4AEg5Vf6wTSjDh9zDpu5nh+5nBNQNKI2tcmkL60Yhi3lSzjHL4O7PoL2PudyZhlkxqYflJEhDcAXLYrj+CqBNWsk9bBrV2lUEowxeBJpW7s2NoMPYoTfeks6Va1cKU8ff//7vhv84mKRRXDit98iF1dFQ1qarPr9hWGxcuqp7hNROK2g6oCXPP0lQGelVCNgj9Y6QRnKlcMBNORNTmMzu9jJHgqow2q2cQwvs5R1yR5e9ebKhXDJYvhgf8nkufdwYC/e1h4Bysr2LU/ezc1RWaxYIVILw4fLin/CBJg4MXZj7pWuXcP3s42VrCwxoGvXhm6rXVtW5PGmuFhW6++/Lw1aVqyQKuATTpBG78ETwu7dAXdOx47SoevAAyXbyKma2Un3aO9eSYnNz5entaqMlx653YEbgYZAmvLJ4qVinn40NKA2DRBVqdbU413O4H7m8Q1rySWLZuTzC5v4DBfHnwMPM5xbmMOOFOrAlXL80BhmHghTDvG9UbkFWy1aSGl/JOmCRLNypbhEliyRzJOKSp9paYaCgrS4TQTNmiUu8yY9HYYNcw5E9+8fPgU0WoqK5Do+/FAK63bvDty7X36ReoENG6QjGIgG/9lnl8/xf+op0Rtyk3X+/HN5OvEHth9/XCqgly2TgPHQofKkmRPaS71K4MWn/wKSs78UhyKt6kJr6vGYKD7/xTx+pT/Pe9o/HRhAazKjMGJNyOVieqDZyGskQCA81fgjD4aNgQ15yRvCH4mrpo2WKVPcM3aM2TeD37Wr6NuvXy+ukMsug34xd7aOzAMPiBGeObOUXbsyycyU8z31VHzPc+aZhOj1B2OMZBnddBM0bOhcybtgAVxzjXuxV3GxKIIOHCjf0bXXBvz827ZJXv/WrTJZV0W8GP2dWuvHEj6SFKQ3LVE0RBM5raIHBRxMM5qRxxZCI0HppHEyB9Hi173saZ1PPWpxMT3YnwbMZzVv8AOl1XdOFam+x3sl1eD7CRfEg8jSx/EgOztyGmWs5OeLlpBfTrkyyMkRY/vKK5p16zr9pdHjxe9uDMyYIYJpeXlw4YXO/XgLC70F73//XY7Vvr0YeCc+/DB8ZXU3n+LY9OkuKquz4IcfJIvMz/r10p944UK57r59IyuJJgMvRn+2UuoKYDYErJnW+teEjSpFyCKDzjSLaPQbksOVHEo2GRxHBzShv2l/pyOvcCqL1y+mR+vy7c360opDacW8CK6kTNKq7sRg0mCCQ8VPkkhPd2+eUhlGv2FD+PPPyJ8DdyVLN/bskTqAgw6SPPvJk2VV+scfsvo/++xAUZcbZWUix1CvXnQ+7A4ddnP66d4/X1Ii2UPvvBP4PiZPlvTWCy8s/9l587zFYmrXlmDvli3uVcjhMnRyciTLCWDVKufPbNsmE4vf6BcVSRFesPjc55/LE8MHHyS+piQavBj9Mb5/rw56z0DNSHnpRQGv84PjtjpkcyIHch7dGURbAO7hSIrYw5v8yB/soCG1GcYBTMahLNNHGmlM5CjO522+QaJhGaTRmFyySMcgTx2/sY1FuNejNyePAuqwlHWUpFpaapaBG+bB+NTQ78vMdK+8jLaTVix4NfggqZWjR4uhevJJdzVNP8XFshLdsEFW+3fcEbjWH38UY7Vtm+TVOzFhggir/fyz+LCbNpX+A02bwqWXikHVWoq+unXbt361//qXaPYHs2GDrJBPPrl80VXnzhI0jnT9AweKJtCePfLvdw7N87KzZdxOfv3gauTmzZ0Nf+3acMghgZ8nTgxVGwWJDTzzjBSXpQpesncSEHuvOpzOwTzAfDYS+ix4Nl15tEIcIJN0nuAY7mQw37AWRWMKcGlEGkRPCljEhUxjCavZRk8KOI4OGMBgyCCdSXzBYtaEmPM8srieflzPADJJZy4reZQv0GykPjkcTTsW8jtvkWQZwoEuy6YkEK7UPtpVfoMGEiBORN9WkNX6ffeJMZw+3V1YrSKLFkl6ZsVr3bVL/NR164au+CdNkrx6v2HdsUOMnj89dupUMYQ//STH7dJFdPbPOy+2a3MrhFqzRrJyrrsu8N6QIVLp+/HHzvtkZ8v2p32qYVlZogA6blyorPTmzc5PMI0by73xc8opcu0VFwKDB4uEhp9wYnCLF7tvSwbhVDbHa63HK6Wex1llM8avuWrRmnr8g17cyzyKCViDnrTgJg533a8RuQwmuvkymwwupLzrJy3ov1fQm1Vs5UW+Zh07SUNiCZMYQV9a/bXPINr+9eThZy+G+/iMm/k4ec8A2Qn2mcSJaKUDjj1WVsD9+iWmefvq1VJ1u2NH5EbiFXFKowRxR1x2maykg4XKXnop/Ep606bylcPffCNNX2bMCGTMREO4c1X0uaeliaroZZdJJfaOHRKkHjFCUjEPPliM/r//LSvrP/8Ud9Y//ykr8YqxnOJi2b9VK/j995106ZLLpZcGpKNBJoAtW6TieMUKcXcNGRKYWPzk57tfR7htTuzdK9IcderEVm0ciXArff/8NDf+p61ajGcwvWjJv/meHRRzMM24ij7Uo3JzttJI40GGcS19eYMfaUkdjkOR7kGRMZ00bmAA6yhiIg7PoZXB0iah7+WUwO4UcniGYb/9xMUSrFnfs6e4KJx6xUbLQQdJcLAiiQr4bt0q+et+o19aKk8G0VJWBu++K5lId96ZQw/fumXXLtHfWbRIVuHDh8sTQXBwt3t3kWuoSL16UjhWkVatxB20cqUUfh1ySPk2jo88Ik3d/RNGYaEEh92C91u3Sp9hrX+gR48eIdu3b5eJY9w4cXG1by9jqMjpp0v1ccWJqm5diaF45YUX5BqWLBEX0oAB4m7r1Mn7MSLhavS11v6uoWdorWMQLa1eHEMHjqFDsocBQAvqcBm9Ytr3QYaznp28ylLKgh7gcsigJy25mEP4hFU8x5Jy26MhDZfc3vxi6LYGvm4OuXugxXb4KfV7IGRlSVBxwwbRu8nJkRXyscfKCvLzz2Wlvy+r/NatnQ2+V8IFpsMRbOQzMyWfP5p4QzDLl8P06c0YNSrQ7Su4Gvc//5Fg7HPPBd678UYx+l99FXgvIwPOPVcKqNxo2zY0w6ekRILAFQ1vUZF7MLxuXfd8+4kT4eGHA/eofXvprjZmTOhnjzhCrmXSpMCioHlzmSwc5hJHPvxQXGV+zSd/8dnatfI7Fq9gsJcQTG2l1H5aa+9VSpaIGAwfsYJlbGQ4f+NvNKyU86aTxnROYjQHM4ufyCSdkXSkC83IJYs00hhNF4bRjvdYTil7WcFmPjOrvUq8M4wDmO0kcnfG9zDqBzovO5DxdQ7jLNWqSnQ+6NpVsjA+/zzw3u+/izLmRRdJznek4GI4GjQQBclYVtl+9t9fAq/R0qJF+Z/795eVb6wsWybL7gkTQuUXjBH30ejRYiRB3BezZ4tr6NtvZVV+3HGSj+/f57nn5F5v2yZPQ1dfHZCBDmbpUve4Snq6c6xm8GDnQPScOWLgg3sZL18u33WfPjIBVOTmm+VJ5sUXZeI666zQXsHheP55Z5G/r76SbRdf7P1Y4fBi9JsgjdDXAbvwLeS01jUieycRLGMDFzKT+aymFEMDcjiRA5nMcVEVd8VKGmmMoD0jcPjN9W0/lY6cioiQGAz3rpjJsgP2sptSyjDsTwM2s4tXWcr2oArk7jRnAsP4mun86aDL1zmjMUsOOpUM0nm0V3x1WRJFy5aiWVORNWuk6ca+qHGmp8MTT0jKoltOuRfcfPfhyMwMXbXeeKNkm8SqKZ+TI48bCxc6by8pEVeQ3+iDGMZ7K3bg9nHddbLi9hvsOXOkIcyMGaEaOY0aubtyGjYU//3ChTKRZGdLR66JE53P++KLzs3r16+Xp4kHHnDer6BAVvexEK5RzC9xFAr2YvSPit/pLAbDRbzDJwSWdZvZzfMsoQX53M0RYfZODmmkMWxzS24g9Dn1XLoxja/Zym460oQr6UMdanE8BzKZ8mkL6cBIOpHhm9guvFAyG5z+uFKBRo0khzzYZ1yRWF0hfurXh0GD5MnhpZdiP04s99CY0OKpggKRVHjnHff9srLE/+/kLunZcxuQHRB5uwAAHV1JREFUH1Yx1IuaKIjf/vnnQ1foWsP990smUTBt2ogP/P33Q4915JFiyN98U1bshx4q992NcBN5tMF0rzjFCvx0iKNn2YvRXwNcDgxB6irfA6JQ2rYE81XeJua7FGG9x/KUNPrh6Mt+9CW0DeJjjCCHDN5hGX+wg7bU5+904pagjKfRo+XfKVMkWOkvivr998RkwUSLUpIqOWVK4s7RoIG4VBIttuZEWZl8B488Ur4Q6uGH5cnBTcW0WzfJarnuuoBLKjtbnnouuuhPoIAhQ5zlEurUiVwY5uf11917DCxZ4vz+pEny9OLPmc/IgMMPl2tMT5fc/0hs2SITjhvx1BLys3atLC6cxAB79RJXUbzwYvSnALWBZ5DF2llAZ7B9CWPh15wdrnr+mx3kG6oqWWTwCCO4j6FsYhdNyCWLUInH0aPlFbzqHDIksc1RvDJ/vqywPv9cDP++BFqdyMyMzQ8fDYcdJobzR5cGp7t2if99zJhAQNPfvtHN6H//vRQwLV0qqYtbtsiq+Ygj4MsvZba+7DK5f6+9Flip5+fLZNG1q7exV+w5EIxb8LV9ewkWv/aarOi7dIHjj/cuw1xSIjGFL7903t6nj2TjTJokMYj69WXC3JeV+MSJ4t7yu+gyMmS8desGsne8Ph15wYvRP1Rr/VccXSk1ExFfs8RAn61NaERtx2KvDkTZ860KkEOmp+K04D/KoUNTw+iD/CEedZRkk0yeLLnabk8hXbrItnr1xBAujfBX4qVZ+r5yww2Brl1uLFsmaZDBKZIzZ7p/vqhIMnPefbd8IVMwGRmSwjhqFLz3nqQfjhlTvqApEmPGiMFbvjx0WzjXTEYGnHaa9/ME88wz8NlnztsOOUSCysOHlw/qT5sm44xlNf7111J9HOwyKiuTJ6dJkwJPw/HES9RwhVKqXdDPzYAkPIxWD5qX5jKS0C4N9cnhEqL4i6jG/OMf5YWs3EiENrwTWotG+y+/uEsONG0q1aXffCMyBw89FJ/VWXqEv9BIujjz5kmgNNyqueJxNmyI3K936VIpgLr/fkk1dJoIn3tOVrD/+Y+IsZ1xhrhX7rnHu4bOvfdKOqufzExZid9+e+T9Y8HNbQQSCPan6Aazfj3cdVd4ATc3pk1zjhGUlCSuM5yXlX4W8LVS6hPEp98f+EMp9TFUfV39ZPA4x9CCOrzDMjayi/Y05BJ6cCIeLF0NoE4dCWr6c7hLSsSvec454lr4+WfJ0b7gAlkNBud4QyAnOy0NWrbczW+/RS6ia9DAm7yBW2pm06blm6j7NddvuGHfAn/Nm7tndfTsKYHOfv3c5aLnzhU3RDhl0c6dxR/vZ+9ebzGVd96RV1aW6N28/HJg20svSc558HnXrpVV+xtvyH7vvivukXCcfLKs6p9+Wu5j//4y1n3t9uVGuMmxTh3nQjKQ63rllejlKMJ9L5HUYGPFi9G/s8LPExIxkJpEOmncykBuZWCyhxIzpewlgzTSvCbvR0n37iIatm6dGNqCAuc/9AULxO3yzTfy8ymnSA73rFlSQduixXeMHdvDNYUQJGA7aZKU88cqtrZqlYzV3/Lwo4/ERdWhgwRpnQx3mzbuKo5+Dj7Yed/sbFlld+4sxT9u7rDVq8Mbj+bN4bbbyj/BNG0q2S0ffhh+bH727JHP+nVuQCajcOedP1+anLulPgbTsKFMniUl8r2+/ba4l+Lp5/Zz/vmy+q4YQM7NlScVJ1E1P7GkufbuLb+/TnTpEv3xPGGMSdlXYWFh28LCQrN7925TkcLCwpD3qgJVfdyTTaE5zDxrmpsJppN53Fxv/mtKTVmSR+dOYWGh+eYbYwYMMCYryxhZwwZebdsaM2eOfPaUU0K3e33Vq2fM+vVynP/8x5iGDctvz8w0pkED+f+sLPn/E0805uijjalVy/2406bJ5yoe6/LLA9d47bXu+7do4b6tZ09jVqwIHKcs6GucO9eYNm2iuwdNmhgzZ85Xxhhj2rWL/PnDD/f+PU6fbkzHjoF9O3Uy5uWXve27fbsx48YZ07+/Mf36GXP11cZs2hT6e+LnqaeMad26/D285x7ZdtZZztfSurUxW7d6vx4/e/YYc+SRocfr1cuYbdsi7+9mT3bv3m0KCwtNYWFhW1PBribdsId7WaMfO2Vmr5lg5pkhZqrpZSabs80bZon5I+bjFRYWmqdNockxdxnM+HKvjuYxU2RK4jj6+OG/33v3GnPHHcbk5ZX/46pVy5j77gt8/tpryxvnjAxvBm/4cPPXeQ4/3PkzdeuGvteqlTF3321MnTqh2w47zJjSUnk995wxZ59tzPnnG/Pmm3IeP1ob07hx6P55ecaMGOE+5qlTxeiMG2dM587GFBQYM2hQwJiuXGnMNdcYc+ihxuTmRr4HWVnGzJz5jTFGxh7p84MHe/sOv/nG+fqaNDHm++/D71tcbMzAgc73tqgo9PfEz9atxjz2mDEPP2zMunWB93/+We5V8LHy84156CH3MezeLffZjaIiY66/XhYmhx1mzNix5c8ZDmv0qwCVNe6LzMwQ47y/mWi+jNHwFxYWmr5mSsgx/a+jzPQ4X0F8CDb6bsa4fXtjdu0K7LNjhzEzZxqzeLHzyi49vfzP7doZs2CB7LtlS+gqP9Jr1CgxtH36GFO7thizIUM2mlWrnK9p+3ZjrrtOJij/6nLqVGP+9rfAMffbz5gJE4z58ktjWrYMPWe/fjKZnHNO6LY6dYx55ZXy51y3TiaHvn2dJygwpnt3Y774Qu73Qw9FnjBvvtnbd3jFFe7HGDs2/L6PPuq+7733hv6eeGH9emNuvFGeDM87z5iPP3b+3Ny5Muk2aybfx+mnG7N6tefTeCIWo78P7Q8sqcoPrOf/OWTVrmALDzKfF/FQoVKBPWl7+Rn3SOdHrGAev9KP1q6fSSY7djg30wAJwi1YEOiWlJcneeogKpStW4s+zJYtklV0xhmSavfHH1JF+c9/BjRWcnJkf7eiIieWLpXOVqedJsfMy4OfflpB69ahekyXXipZMX7/8aRJEuB++mlJuXz5ZfGxH320+NUffFCC4G3aSPZRTo4EQydMkAIkJ3mJ7dulLiE47fHzz+XYv/3mfA25uZKv7s+ouvJK6bz10kvOhU7DhkkfWy9s3Oi+bdMmUdJ84glJp23aVL4ff6tIt3x7CJ+pE47GjSUeEY7vvpMkg9VBdZivvCLfwaefJiYe4RVr9KshM1nGVpz7xC1lXUzHzDRpNCGXdQ56OgB72Mt8Vqes0a9VS4qDnAxITo6oSzqRmSl9T++skM4wapT7eQYOlJJ/r/gzRtLSwuunz5ghxt2YwHt+ZckjjxTZ5//+V67xttsksOynUSNJfzz33IBhfust94yl4KIxrSVI62Tw69WTQq0xY8TY+huGpKVJGuP114tR3rJFMol27gx83mvHLSdxMz+7domBDw52v/uuGOWxY8NLaOQlsF3zY4+VN/h+vvhCJuOLLkrcuSNhjX41pBG1XbfVIYqGp0GkkcaxdOA71jtuTwfap3BxWXa2pP5Nmxa6rX9/Ee9asEAEuXr0kPdiZeJEMUL/+1+gGrVHD1lBO+W/D/coXH777eUNfjCXXCKG1S37aOPGQOWt3+h37Cj3xSnrpHFjmUTOPVeyZdzy6hs1kuIrtxTK/PxAIdWJJ7peWljGjpUnBqe+Ah98EJquWlQkK/+LLxa1zmnTQrWJcnLcJ+54sGKF+7ZEdVjzSuIlHS2Vzpl0oSPOmq5Do+zmFcxdDKGfg84OQB9acQIq5mNXBhMnSv588Aqzd29ZkR57rMg/XHmlrJpHjAjvVghHo0aSwvj662KoX3hB3CNPPy0pln7y8sQo3Xyzt+O65eKDuDkipZtqLTnyfvr1K9/nNZjjjhNj++qr4QupKqOquEEDkT9wwu2eLFsmTxZ9+ohEcrDEcaNGkgIa3CEr3oSTVG7ePHHn9YJd6VdDapHJQwzjn7zPMsRy5ZLFiRzIjWFaPEYik3Q+5VyuYTbT+YYN7KI2mfSnNY9zdMJy9uNF/frim3//fXFDHHCArPbOOktcAn6Ki+Uzl10mlaSxkJYmbge/bxlkxbt4saxa164Vv7ab0XWibdvwQmBex+Vn/PhQTZ78fNGWueYab92aevVKXKFUMNHESEAmdn+x3LhxMrm+8IJMjGeeWb7KN56sWCFFeStWODdxb9dOfq+SiTX61ZThtGMJFzOVJWxkF8M4gN6E0W71SBppPMRR3M5gPmUVralHZ1wc4ilIWpqs4keMkJ+3bXNvtP3RR7LabxRHr1V2trhMYuG55ySQXFwhXONXJ42EUgEXy6xZoiBa8Vggk+D27SLHEI527bwHY/eVaL+D3r3l5aegQOILiWT+fJlQgl07/o5mGRkyQd5zjwipJRPr3qnG1CaLS+nFzRweF4MfTB1qcTQdqpTBd2LzZvdV5KZNsbt4EsH++4t/vXVrWclmZsr/T5kiVavhaNxYZC38GjuvveZs8HfskCB0kyZi1J3IzYUrrpDJsnv3fbsmkKrkjz8OL1cxZoxzg/FWrcobd5CJ8YEHKucJJJi77w715e/dK9/b//4nk0I4objKIikrfaXUScBIrbVHZW2LJTG0aiW9WP0yDsEcdJD8waYSw4aJkSwtFaPmD8quWydGx+/jTk+XlX337mLwL7ywfDwhnJ++qEiOO2aMyEkHB3rT0yXuUTFlce9eiY289pqiuDjgxvCnvjqxfr1ksXz8sYx7v/2kac0DD4QKzQ0dKkqhkyZJQDctTXT9775b7skrr4jGUEGBXGu4rJ1EsGuXZCk5sWKF3J/KnoTcqHSjr5R6BBgOxJgla7HEj4wMEcn6v/8rv/KtVUvy35OZTx2OiumO48aJBPT06ZJ1M3Cg6BC5GZquXd3jFf6V8zXXSJbLSy9Js5QWLeDUU53bAV52mQSqQZbjy5cH0hOPP975POedV75D1+rVok5arx7cemvo56+4QkT23nlHVv3DhgUmPX9P3WQRPAFXJD09shpqZZKMlf584E0gTm1+LZZ9Y+xY8bO++KLkohcUSM55cDepWFm6VATX+vdPbF44iECXFwEzkGueOTO0L++QIeWVIi+/XF7GuE8gv/4q8skV2bQJnnzS2eh/+617LOWtt5yNPojc8siRztuSSU6ONKx5/fXQbb16yStVSDNuib/7iFLqfOCqCm+fq7VepJQaBFyitQ7b6mDx4sVtgTAZrxZLarJyZTb339+aJUvyKSnJoGXL3RxzzEZfO8HUYNu2dJ59tgVLl+aRnm7o0qWICy74k9q1o5Maff31xtxzTxvHbQUFxbz9dmh1+HvvNeDWWw9w3Cc9fS+tWxdzyCHbGTv2d/LyYpQ+rWRWrszmhhv+xvLlAd9SixbF3HTTKvr02e7pGLt2pfPqq01ZsSKHvLwyjjtuAx07xiDUH2D/Hj16rCz3TkVdhsp4dejQYVCHDh1ejfQ5q72TOvjHvdXsNpvNrgifTh2Scb/LykRHp6LWS3a2MZMneztGVfo9+e9/RfnTSd/mkEOc91mxIiBsF+41bJjoJn3wgWjddO9uzNChxjz9dHyvIV73e8cOY+6/35iLLhJtobVrve+7Zo0onwZff/36xjz5pPs+VnvHkjB+zNnCbbzMF/xGGYbetORmDqd/isgulJWJ7zQVgmUzZjjrrpeUSNZMPNxGqcQRR0gRlFObwaOOct6nbVspAHvhhfDH/ugjSQt95pnyKaRz54rbLFEdtGIlL08axsfCrbeGBoO3bJHU2jPPdM5eigWbsmmJyGZ2cfMBX/Euy9nALjazm9n8zBhmsIotSR3bO++IjEHr1pJtc+mlies45JWff3aXS1gXm/RRSpOWJkHcfv0gPV0uvH59qUcIZ5SfeUbE6tq3d9fhKSuTzJyKNQN79sDUqVJPUF1wa9CycmX5rmT7SlJW+lrrucDcZJzbEj2T+IKVtUMt6Uq2MokveBCP4jFx5uOPxbAEGwStJRgbrrF3ojn0UHdNm1RLAY0XHTuKeuRjjy0nPb0DRx4pKaPhyM6W6tUHH5TK5ffec/6cW63Er7+KwNzJ0YvGpiThwqvxDL3alb4lIqtwr5pZTRhBmATz5JPOVaMffCBugWQxaJCzrkuDBpJyWF1JS4PDDtvO5ZdHNvjBZGZKtpRTWuNBB7kroGZlQcuWsY01FXHTF2rbFkaPjt95rNG3RKQZ7rmG4bYlml9+cX6/pEQEzpLJq69Kv9UDDpDiqIEDxQUS3IDcEmD0aDH89erJz+npkub49NMSM3DisMNCq3GrMuPHixprMPXrS4wgXv58sNo7Fg9cQW+mFn/Jn7XKp441J59L6ZmkUYkxdaNt20obhiP5+SKPUFoqk1BlV4hWJXbvhtNPF/eO3yXWqpVIQQ8YIKJ0v/8uT3D+7b16waOPpkbgPl60bCkB6kmTpBK6bl0pEIx3jr81+paIFFCXW1Z24SW1hoX8xl4MPWnJ9fSnI02TNq5TT4U5c0KVDHv0SKxWejT4NXIs7tx0E7z5Zvn3fv0VrrpK+hvk5UmMZs4ceYJr21a6hLlVwFZl8vNFIymR2F9HiycO3dGESxnO96ynlL10oVnSpZQvvFCCtlOnipHIyhK/6KOPWkNblXCrzP3qKxGYO+kk+Xnw4EBLS0vs2D8Ni2fSSKNTElf2Ttx+u2jEzJ4tLoE+farXI39NoGJXKz/GwJ+pU8BcbbBG31LlqVs3NfVYLN7o3Nm5FWKzZtUnHTOVsNk7FoslqVx9tTylBeOXdnZL17TEjl3pWyyWpDJggKhTPvqo9LZt0ECKtS65JNkjq55Yo2+xWJJO797SC8CSeKx7x2KxWGoQ1uhbLJYqwfPPS0P7nj2lRuPDD5M9oqqJde9YLJaU5+674Y47AhW5ixdL9eqUKXDiiUkdWpXDrvQtFktKU1QEzz4bqlq6caMEfy3RYY2+xWJJaebOhRUuTVOXLhXtHot3rNG3WCwpTUGBaO87UaeOyG9YvGONvsViSWm6d5euXE4MGlQ9hdcSiTX6Fosl5Zk0qbzEcHa29N996KHkjamqYrN3LBZLytO5MyxYIM1pVq6UCWDoUCuuFwvW6FsslipBRkZ82wbWVKzRt1gsKcPq1TB5MmzfLjLZf/+7tE60xA9r9C0WS0owfbr0g127Vn6eNEka5LzxBtSundShVSvsHGqxWJLOjh1w660Bgw/SRGX2bHnfEj+s0bdYLEln6lQJ0Drx6aeVOZLqjzX6Fosl6eza5b6tovyCZd+wRt9isSSd006DRo2ct/XoUbljqe5Yo2+xWJLOfvvBxReHyi107gw33JCcMVVXbPaOxWJJCe6+G7p0gRkzJGWzUyfpn9uiRbJHVr2wRt9isaQMo0bJy5I4rHvHYrFYahDW6FssFksNwhp9i8ViqUFYo2+xWCw1CGv0LRaLpQaR6tk7GQAlLiV5xcXFlTqYeGHHXbnYcVcudtyVi9O4g2xmSF+xNGNMgocUO4sXL+4PWOUNi8ViiY0BPXr0+Cz4jVRf6S8CBgB/AGVJHovFYrFUFTKAFogNLUdKr/QtFovFEl9sINdisVhqENboWywWSw3CGn2LxWKpQVijb7FYLDUIa/QtFoulBpHqKZuuKKXqAS8CdYFs4Gqt9YLkjso7SqmTgJFa6zOSPZZwKKXSgSeArkAxcIHW+qfkjsobSqlDgfu01oOSPRavKKWygOeAtkAt4C6t9dtJHZQHlFIZwDOAQtKrz9Va/5zcUXlDKdUUWAwM1Vr/mOzxeEUp9RWw1ffjCq31uV72q8or/auBj7TWA4FzgMeTOxzvKKUeAe6hatz/E4EcrXVf4HrgwSSPxxNKqXHAFCAn2WOJkjOBjVrrAcAI4LEkj8crxwForfsBtwIPJXc43vBNsk8DYbr0ph5KqRwArfUg38uTwYeqYXTceBj5skCeWHYncSzRMh+4NNmD8Eh/4H0ArfXnQM/kDsczPwMnJ3sQMfAacEvQz6XJGkg0aK3fBC7y/dgGWJvE4UTDBOApYE2yBxIlXYFcpdQHSqmPlVJ9vO5YJdw7SqnzgasqvH2u1nqRUqo54ua5svJHFp4w4/5/SqlBSRhSLNQl8AgJUKaUytRap7Qx0lq/rpRqm+xxRIvWegeAUqoO8B/g5uSOyDta61Kl1DTgJODUZI8nEkqpc4D1WuvZSqmq1ol3JzJhTQHaA7OUUsrL32WVMPpa62eBZyu+r5Q6GHgVuFZr/b9KH1gE3MZdxdgG1An6OT3VDX5VRym1H/AG8ITW+uVkjycatNZnK6X+D/hCKdVRa12U7DGF4TzAKKWOBLoBLyiljtda/5nkcXlhGfCT1toAy5RSGxHZhdWRdqwSRt8JpVRH5FF4lNb662SPpxozD/HX/tv3CPltksdTrVFKNQM+AP6htf4o2ePxilJqDNBKa30PsgrdS4rrZWmtD/f/v1JqLnBJFTH4IBPWwcBlSqkC5In8Dy87VlmjjwRCc4BHlFIAW7XWJyR3SNWSN4ChSqn5QBrgOWBkiYkbgQbALUopv29/hNY61QONM4DnlVKfAFnAlVrrqhRnq2o8C0xVSn0GGOA8r0/gVnDNYrFYahBVOXvHYrFYLFFijb7FYrHUIKzRt1gslhqENfoWi8VSg7BG32KxWGoQ1uhbUgal1PFKqTti2K+nUmpKjOccr5QaH8u+VQWlVG+l1H1htrdUSnnK8bZUfapynr6lmuFTk4xaUVJrXQhcEP8RVRs6As2cNiiljgYmAs0rdUSWpGGNviXh+HSGbgH2APsDCxEj3QIRc9uAqBy+BAzSWp+jlFoJTAeGA3nAWVrrxUqpbojQXi6wCRgNtAPGa60H+SorlwCHI8V7V2qtP1BKdQYeBfKBpsA9Wuunwoz5DET3xgCLgAuRoqNnELGrvcAErfULPg2XY4BGQIFvfG2AIcBGRC2zOTKh/Qh0AlYBZ2qtNymljgXuQp68fwEu1lqvDXMP2gFP+s63E7hCa/2VUmoqopPUA2gJ3IEU190B5CulbtJa313hUs9HhOlspXUNwbp3LJXFYcBY4EDEGF/ue18hxm+owz4btda9ERXEG33vvQTcqbX26y6Nddivrtb6EOAMYJpSKhuZZO7SWvcCBgMPuA1UKdUSUXEdprXuBGQgRn28b0ydEYM+XinVxbdbb0SGejgiKzxLa+3fNtz378GInk4n4Aff/k2RSeJE3+fnUV5O2ekeTAPG+a7xIt998LMfMAA4HpmUtiBSx287GHy01qdorZe63QtL9cMafUtl8YkWDLJ6HeJ7f53WeqXLPu/7/l0KNFRKNQZaaK3fAdBaP6m1vs5hv2d825cgeiRdgGuAHJ+a4l3Iit+NvsA8rfVvvuOM8UkHD8EnoKe13gC8BQzy7TNPa71Na73K97NfN2cVIqsAsExrPdf3/9N8x+sNLAy6B5OBI8Lcg3ygFyJ5sAR4GVnFN/J97gPfPV4KNAxzjZYainXvWCqLYF2Q9KCfw2nK+LVbDKL7s8f3/8BfjSQKPJ7r38BmYCayMj49zHkrnqdJ0LGCSSPwN1QSvMFFB8VpXOGOCaH3IAPYrbXuFjS+Voir66/Pa62NT5PKYimHXelbKov+viyRdOAsYFa0B9BabwV+U0oN8701BvFXV+Q0kKweZJX9LTAUuFVr/RbiY/e3+HNiEdDH16sBxNVzAvAx4gPH99RxIjA3iktQvpgEiHDdLOAL37na+t6/CJjjdgDfPViulDrTd8ChwCcRzluKXeBZfFijb6ks1gAvAN8DvyPNH2LhTOBWn2tjFODk3jlAKfUl4ioZpbUuQ/zxnymlvkd83iuRoHIIWus1SKxgtlJqKfI08jwywTRUSn2LGNq7tdZfRjH2TcDtSqnvkGDyXVrrtYihf8P3/iDgkgjHGQ1coJT6BlGbHeVz6bixEJlY7o1irJZqilXZtCQcX/bO+MpoUO7L3hkf5DtPCXwr+bla67ZJHoqlhmNX+haLxVKDsCt9i8ViqUHYlb7FYrHUIKzRt1gslhqENfoWi8VSg7BG32KxWGoQ1uhbLBZLDeL/A3OCQBfSmcnbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusters = [2,3]\n",
    "\n",
    "for cluster in clusters:\n",
    "    \n",
    "    print('-'*100)\n",
    "    \n",
    "    kmeans = KMeans(n_clusters= cluster, random_state=0).fit(pca_dataset)\n",
    "    \n",
    "    # define the cluster centers\n",
    "    cluster_centers = kmeans.cluster_centers_\n",
    "    C1 = cluster_centers[:, 0]\n",
    "    C2 = cluster_centers[:, 1]\n",
    "\n",
    "    # create a new plot\n",
    "    plt.figure()\n",
    "\n",
    "    # take the scaled data in this example.\n",
    "    x = pca_dataset['principal component 1']\n",
    "    y = pca_dataset['principal component 2']\n",
    "\n",
    "\n",
    "    # define the axes labels\n",
    "    column_names = pca_dataset.columns\n",
    "    plt.xlabel(column_names[0])\n",
    "    plt.ylabel(column_names[1])\n",
    "\n",
    "    # Visualize it:\n",
    "    plt.scatter(x, y, c=kmeans.labels_.astype(float), cmap='winter')\n",
    "    plt.scatter(C1, C2, marker=\"x\", color='r')\n",
    "\n",
    "    # Plot the clustered data\n",
    "    plt.title('Visualization of clustered data with {} clusters'.format(cluster), fontweight='bold')\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step Ten: Make Predictions\n",
    "Okay, we've settled on three clusters being our number of choice let's make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the model\n",
    "kmeans = results_dict[3]['model']\n",
    "\n",
    "# define some test data\n",
    "test_data = [[0.10, 3.10, 5.20],\n",
    "             [0.60, 5.10, 2.20]]\n",
    "\n",
    "# make some predictions, in this case let's pass through our test set.\n",
    "kmeans.predict(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Notes:\n",
    "\n",
    "Overall, I was happy with the results, but this is not to say there couldn't be extra steps we could take to improve the results. This final part of the tutorial is taking a step back and looking at some things we might want to address or explore deeper.\n",
    "\n",
    "***\n",
    "### The Clusters Appear Very Dense\n",
    "This could impact the K-Means algorithm, and while it might not provide perfect results they could be suboptimal. Because of this, I would like to explore more density-based clustering algorithms and see how that impacts our results.\n",
    "\n",
    "***\n",
    "### Exploring How Filtering Affects the Results\n",
    "While I did my best to apply filters that seemed reasonable, I would be curious to see how our results change as we change the filter for outliers."
   ]
  }
 ],
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